├── dictionary ├── 简明英汉词典（vivo_edited）.csv └── 简明英汉词典（vivo_edited）.xlsx ├── README.md ├── log.py ├── vivo ├── diff_words_select.py ├── word_frq.py └── auto_trans.py ├── vocabulary ├── simple_words.txt ├── highschool_edited.txt └── WordList_TOEFL.txt ├── lemmas └── lemmas_qs_extra.json ├── lemmas.py ├── all_text_trans.py └── tests ├── driveless.txt └── driveless.txt-trans.html /dictionary/简明英汉词典（vivo_edited）.csv: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mahavivo/vocabulary/HEAD/dictionary/简明英汉词典（vivo_edited）.csv -------------------------------------------------------------------------------- /dictionary/简明英汉词典（vivo_edited）.xlsx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mahavivo/vocabulary/HEAD/dictionary/简明英汉词典（vivo_edited）.xlsx -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | 计算机词汇分析辅助英文阅读可能需要的词汇表。 2 | 3 | 主要设想见：https://www.douban.com/note/635626889/ 4 | 5 | 6 | 1）COCA 20000词频表、COCA 60000词频表搜集自网络，如若侵权，告知即删。 7 | 8 | 2）高中英语词汇表整理自“2016高考全国卷英语考试大纲3500词汇表word版”（ https://wenku.baidu.com/view/1c8c3292f7ec4afe05a1df22.html ）。 9 | 10 | 3）大学英语六级词汇表整理自“全国大学英语四、六级考试大纲》2016年版"( http://www.cet.edu.cn/file_2016_1.pdf ）。 11 | 12 | 4）托福、GRE词汇表来自我个人保存的资料，源于2003年版金山词霸，十余年未接触研究，不知是否依然有效。 13 | 14 | 5）Lemmas文档来自 https://github.com/Enaunimes/freeq 。 15 | 16 | 6）简明英汉词典收词10万个，下载自网络，原来标称“牛津简明英汉词典”云云，似于事实不符，其释文可能来自网络上的各大在线词典或（及）其他词汇表。我稍微予以编辑，比如删除无释文的词头（部分酌情补充释义）、释文中发现的乱码用在线词典修正等。 17 | -------------------------------------------------------------------------------- /log.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python3 2 | # -*- coding: UTF-8 -*- 3 | 4 | import logging 5 | 6 | def log(logger_name): 7 | logger = logging.getLogger(logger_name) 8 | logger.setLevel(logging.DEBUG) 9 | 10 | if not logger.handlers: 11 | fh = logging.FileHandler('auto_trans.log') 12 | fh.setLevel(logging.INFO) 13 | 14 | if not logger.handlers: 15 | ch = logging.StreamHandler() 16 | ch.setLevel(logging.INFO) 17 | 18 | formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s') 19 | fh.setFormatter(formatter) 20 | ch.setFormatter(formatter) 21 | 22 | logger.addHandler(fh) 23 | logger.addHandler(ch) 24 | return logger -------------------------------------------------------------------------------- /vivo/diff_words_select.py: -------------------------------------------------------------------------------- 1 | # -*- coding: UTF-8 -*- 2 | 3 | with open('allwords_freq_jane_eyre.txt', 'r') as aw: 4 | word_list = aw.read().split('\n') 5 | 6 | with open('coca_60000.txt', 'r') as coca: 7 | coca_list = coca.read().split('\n') 8 | 9 | with open('cet6_edited.txt', 'r') as cet: 10 | cet_list = cet.read().split('\n') 11 | 12 | 13 | diff_list = list(set(word_list).difference(set(cet_list))) 14 | 15 | temp_dict = {} 16 | for x in diff_list: 17 | if x in coca_list: 18 | p = coca_list.index(x) 19 | temp_dict[x] = p 20 | 21 | # diff_dict = sorted(temp_dict.items(), key=lambda x: x[1]) 22 | 23 | diff_dict = dict((k, v) for v, k in temp_dict.items()) 24 | 25 | # for k in sorted(diff_dict.keys()): 26 | # print(k, diff_dict[k]) 27 | 28 | for k in sorted(diff_dict.keys()): 29 | print(diff_dict[k]) -------------------------------------------------------------------------------- /vocabulary/simple_words.txt: -------------------------------------------------------------------------------- 1 | # 26个英文字母 2 | a 3 | b 4 | c 5 | d 6 | e 7 | f 8 | g 9 | h 10 | i 11 | j 12 | k 13 | l 14 | m 15 | n 16 | o 17 | p 18 | q 19 | r 20 | s 21 | t 22 | u 23 | v 24 | w 25 | x 26 | y 27 | z 28 | # 罗马数字 29 | ii 30 | iii 31 | iv 32 | vi 33 | vii 34 | viii 35 | ix 36 | xii 37 | xiii 38 | xiv 39 | xv 40 | xvi 41 | xvii 42 | xviii 43 | xix 44 | xx 45 | xxx 46 | xl 47 | lx 48 | lxx 49 | lxxx 50 | xc 51 | xcix 52 | an 53 | is 54 | are 55 | was 56 | were 57 | be 58 | do 59 | did 60 | it 61 | they 62 | them 63 | he 64 | him 65 | his 66 | she 67 | her 68 | i 69 | me 70 | you 71 | not 72 | the 73 | this 74 | that 75 | doing 76 | making 77 | going 78 | cannot 79 | coming 80 | higher 81 | working 82 | according 83 | # 第几 84 | fourth 85 | fifth 86 | sixth 87 | seventh 88 | eighth 89 | ninth 90 | tenth 91 | eleventh 92 | thirteenth 93 | fourteenth 94 | fifteenth 95 | sixteenth 96 | seventeenth 97 | eighteenth 98 | nineteenth 99 | mr 100 | mrs 101 | www 102 | http 103 | unknown 104 | mama 105 | unhappy 106 | km 107 | # 常见人名 108 | ada 109 | ann 110 | anna 111 | ella 112 | emily 113 | helen 114 | isabel 115 | jane 116 | judy 117 | lee 118 | lisa 119 | lucy 120 | mary 121 | max 122 | sandy 123 | sam 124 | sophia 125 | susan 126 | victoria 127 | vivien 128 | zara 129 | addison 130 | alan 131 | albert 132 | allen 133 | andy 134 | barry 135 | ben 136 | bill 137 | bing 138 | brian 139 | calvin 140 | clark 141 | dave 142 | david 143 | dean 144 | dennis 145 | dick 146 | don 147 | donald 148 | dylan 149 | eric 150 | ford 151 | frank 152 | franklin 153 | hamiltion 154 | harry 155 | hilary 156 | hugo 157 | jack 158 | james 159 | jerry 160 | jim 161 | julian 162 | justin 163 | john 164 | kent 165 | kim 166 | king 167 | larry 168 | lawrence 169 | leo 170 | levi 171 | lewis 172 | lionel 173 | louis 174 | mark 175 | matthew 176 | michael 177 | mike 178 | nick 179 | noah 180 | norman 181 | norton 182 | oscar 183 | paul 184 | richard 185 | robert 186 | robin 187 | simon 188 | stanford 189 | stanley 190 | steven 191 | taylor 192 | ted 193 | thomas 194 | tom 195 | tony 196 | vincent 197 | wayne 198 | william 199 | winston 200 | english 201 | -------------------------------------------------------------------------------- /vivo/word_frq.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python3 2 | # -*- coding: UTF-8 -*- 3 | 4 | import re 5 | import string 6 | from collections import Counter 7 | 8 | 9 | lemmas = {} 10 | with open('lemmas.txt') as fin: 11 | for line in fin: 12 | line = line.strip() 13 | headword = line.split('\t')[0] 14 | try: 15 | related = line.split('\t')[1] 16 | except IndexError: 17 | related = None 18 | lemmas[headword] = related 19 | 20 | 21 | valid_words = set() 22 | for headword, related in lemmas.items(): 23 | valid_words.add(headword) 24 | if related: 25 | valid_words.update(set(related.split())) 26 | 27 | 28 | main_table = {} 29 | for char in string.ascii_lowercase: 30 | main_table[char] = {} 31 | 32 | special_table = {} 33 | 34 | for headword, related in lemmas.items(): 35 | headword = headword.lower() 36 | try: 37 | related = related.lower() 38 | except AttributeError: 39 | related = None 40 | if related: 41 | for word in related.split(): 42 | if word[0] != headword[0]: 43 | special_table[headword] = set(related.split()) 44 | break 45 | else: 46 | main_table[headword[0]][headword] = set(related.split()) 47 | else: 48 | main_table[headword[0]][headword] = None 49 | 50 | 51 | def find_headword(word): 52 | word = word.lower() 53 | alpha_table = main_table[word[0]] 54 | if word in alpha_table: 55 | return word 56 | 57 | for headword, related in alpha_table.items(): 58 | if related and (word in related): 59 | return headword 60 | 61 | for headword, related in special_table.items(): 62 | if word == headword: 63 | return word 64 | if word in related: 65 | return headword 66 | 67 | return word 68 | 69 | 70 | def is_dirt(word): 71 | return word not in valid_words 72 | 73 | 74 | with open('shorthistory.txt','r', encoding='utf-8') as ft: 75 | content = ft.read().lower() 76 | temp_list = re.split(r'\b([a-zA-Z-]+)\b', content) 77 | temp_list = [item for item in temp_list if not is_dirt(item)] 78 | stemp_list = [find_headword(item) for item in temp_list] 79 | 80 | 81 | cnt = Counter() 82 | for word in stemp_list: 83 | cnt[word] += 1 84 | 85 | 86 | report = sorted(cnt.items(), key=lambda x: x[1], reverse=True) 87 | 88 | 89 | for row in report: 90 | print(row[0], row[1]) 91 | 92 | 93 | with open('output_file.txt', 'w') as output: 94 | for x in report: 95 | output.write(x[0] + ' ' + str(x[1]) + '\n') -------------------------------------------------------------------------------- /vivo/auto_trans.py: -------------------------------------------------------------------------------- 1 | # -*- coding: UTF-8 -*- 2 | import time 3 | import csv 4 | import re 5 | 6 | 7 | dict_data = {} 8 | count = 0 9 | 10 | print(time.strftime("%Y-%m-%d %H:%M:%S", time.localtime()) + ", open ecdict.csv") 11 | 12 | with open('ecdict.csv', 'r', encoding='utf-8') as ec: 13 | f_ecdict = csv.reader(ec) 14 | headers = next(f_ecdict) 15 | for row in f_ecdict: 16 | # 如果需要将释义中的换行符去掉，则取消下面一行的注释 17 | row[3] = row[3].replace(u'\\n', ' ') 18 | # NOTE: 将词典的 key转换为小写 19 | dict_data[row[0].lower()] = row[3] 20 | 21 | print(time.strftime("%Y-%m-%d %H:%M:%S", time.localtime()) + ", got dict_data") 22 | 23 | with open('shhard.txt', 'r', encoding='utf-8') as w: 24 | word_list = w.read().split('\n') 25 | 26 | with open('shorthistory.txt', 'r', encoding='utf-8') as ori: 27 | all_text = ori.read() 28 | 29 | # 将lemmas.txt转化成两个字典： 30 | # 第一个字典，key是lemmas.txt每行的第一列，value是以list格式存放的lemma.txt一整行内容 31 | # 第二个字典，key是lemmas.txt的每一个单词，value是key所在行的第一列，value只有一个单词 32 | # NOTE: 两个字典的内容均为 小写 33 | 34 | lemmas = {} 35 | re_lemmas = {} 36 | 37 | print(time.strftime("%Y-%m-%d %H:%M:%S", time.localtime()) + ", open lemmmas.txt") 38 | 39 | with open('lemmas.txt', 'r', encoding='utf-8') as lemmas_file: 40 | temp_lemmas = lemmas_file.readlines() 41 | for line in temp_lemmas: 42 | parts = line.split() 43 | lemmas[parts[0].lower()] = [] 44 | for i in range(0, len(parts)): 45 | lemmas[parts[0].lower()].append(parts[i].lower()) 46 | re_lemmas[parts[i].lower()] = parts[0].lower() 47 | 48 | print(time.strftime("%Y-%m-%d %H:%M:%S", time.localtime()) + ", got lemmmas and re_lemmas") 49 | 50 | # 将w_list和w_list所有单词的其他形式，以及中文意思，存到all_words_trans 51 | 52 | all_words_trans = {} 53 | for w in word_list: 54 | if w: 55 | w_lemmas = lemmas.get(w) 56 | for w_le in w_lemmas: 57 | a_tran = dict_data.get(w_le.lower()) 58 | if not a_tran: 59 | org_w = re_lemmas.get(w_le.lower()) 60 | if org_w: 61 | org_w_tran = dict_data.get(org_w) 62 | if org_w_tran: 63 | a_tran = org_w_tran 64 | else: 65 | print('Error, "%s" is not in dict_data' % org_w) 66 | else: 67 | print('Error, "%s" is not in re_lamms' % org_w) 68 | if a_tran: 69 | all_words_trans[w_le] = a_tran 70 | else: 71 | all_words_trans[w_le] = "No translation" 72 | print(all_words_trans) 73 | 74 | print(time.strftime("%Y-%m-%d %H:%M:%S", time.localtime()) + ", begin to replace") 75 | 76 | for w, tran in all_words_trans.items(): 77 | # 替换之外，给生词和翻译加上html格式 78 | w_tran = ''+ w + ''+ '(' + tran + ')' 79 | # 为避免误替换单词，被替换的单词前后必须有以下标点的任意一个: 80 | # 空格,:.'?!@;()\r\n 81 | # NOTE: 如果一个单词连续出现两次以上，则只会替换第一个 82 | 83 | pattern = re.compile(r'([ ,:\'\.\?!@;(])%s(([ ,:\'\.\?!@;)])|(\r)|(\n))' % w) 84 | all_text = pattern.sub(r'\1%s\2' % w_tran, all_text) 85 | 86 | print(time.strftime("%Y-%m-%d %H:%M:%S", time.localtime()) + ", end" ) 87 | 88 | all_text = all_text.replace('\n', '
') 89 | 90 | with open('trans_result.html', 'w', encoding='utf-8') as fout: 91 | fout.write(all_text) 92 | 93 | print(time.strftime("%Y-%m-%d %H:%M:%S", time.localtime()) + ", wrote to file") -------------------------------------------------------------------------------- /lemmas/lemmas_qs_extra.json: -------------------------------------------------------------------------------- 1 | {"aegis": ["egis"], "aerie": ["eyry", "eyries", "aeries", "eyrie"], "aesthete": ["esthete", "aesthetes", "esthetes"], "aesthetic": ["esthetic", "aesthetically", "esthetically", "aesthetics", "esthetics"], "aestheticism": ["estheticism"], "although": ["altho", "though", "tho"], "around-the-clock": ["round-the-clock"], "aught": ["aughts", "ought"], "bad": ["badness", "baddest", "badly", "worst", "worse", "badder", "bads"], "be": ["wast", "been", "art", "were", "wert", "'s", "is", "was", "bes", "'m", "'re", "being", "am", "are"], "beneath": ["neath"], "betwixt": ["twixt"], "caftan": ["kaftan", "kaftans", "caftans"], "caliph": ["khalifs", "caliphs", "calif", "califs", "khalif"], "caliphate": ["califate", "califates", "khalifates", "khalifate", "caliphates"], "chamois": ["chamoix", "shammy", "chammies", "shammies", "chammy"], "chantey": ["shanties", "chanties", "chanty", "chanteys", "shanty"], "chutzpah": ["chutzpa", "hutzpah", "hutzpa"], "cola": ["kola", "colas", "kolas"], "conjurer": ["conjurors", "spelled", "also", "conjuror", "conjurers"], "cow": ["cows", "cowing", "cowed", "kine"], "curb": ["curbing", "curbs", "curbed", "kerbs", "kerb"], "curbside": ["kerbside", "curbsides", "kerbsides"], "curbstone": ["kerbstone", "kerbstones", "curbstones"], "czar": ["tzar", "tzars", "tsars", "tsar", "czars"], "czarina": ["tzarina", "tsarinas", "czarinas", "tsarina", "tzarinas"], "czarism": ["tzarism", "tsarism"], "czarist": ["czarists", "tsarists", "tsarist", "tzarists", "tzarist"], "djellaba": ["djellabahs", "djellabas", "jellaba", "jellabas", "djellabah"], "eat": ["eaten", "ate", "eats", "eating"], "edema": ["oedema"], "embed": ["embedded", "embedding", "imbedded", "imbed", "imbeds", "imbedding", "embeds"], "emir": ["amirs", "emirs", "amir"], "emirate": ["emirates", "amirates", "amirate"], "enclose": ["inclosing", "inclosed", "enclosing", "enclosed", "incloses", "inclose", "encloses"], "enclosure": ["inclosures", "enclosures", "inclosure"], "encrust": ["encrusting", "incrusted", "encrusts", "incrust", "incrusts", "incrusting", "encrusted"], "encrustation": ["encrustations", "incrustations", "incrustation"], "encumber": ["incumbering", "encumbers", "incumbered", "incumbers", "encumbered", "encumbering", "incumber"], "encumbrance": ["incumbrance", "encumbrances", "incumbrances"], "encyclopedia": ["encyclopedias", "cyclopedia", "encyclopaedias", "cyclopedias", "cyclopaedias", "cyclopaedia", "encyclopaedia"], "endorse": ["endorsing", "indorsing", "endorsed", "indorses", "indorsed", "indorse", "endorses"], "endorsement": ["indorsements", "indorsement", "endorsements"], "endorser": ["indorsers", "endorsers", "indorser"], "endue": ["enduing", "endues", "indued", "endued", "induing", "indues", "indue"], "enfold": ["enfolded", "infold", "infolds", "infolding", "infolded", "enfolding", "enfolds"], "entrench": ["intrenching", "entrenching", "intrenches", "entrenched", "intrench", "intrenched", "entrenches"], "entrenchment": ["intrenchments", "entrenchments", "intrenchment"], "entrust": ["entrusting", "entrusts", "intrusting", "intrusts", "intrusted", "intrust", "entrusted"], "eolian": ["aeolian"], "eon": ["eons", "aeons", "aeon"], "escutcheon": ["escutcheons", "scutcheons", "scutcheon"], "esophageal": ["oesophageal"], "esophagus": ["oesophagi", "esophaguses", "oesophaguses", "oesophagus", "esophagi"], "estrogen": ["estrogens", "oestrogen"], "estrous": ["oestrous"], "estrus": ["oestrus", "estruses", "oestruses"], "etiologic": ["aetiologic"], "etiological": ["aetiological"], "etiology": ["etiologies", "aetiology", "aetiologies"], "fantasy": ["phantasy", "phantasies", "phantasied", "fantasied", "fantasies", "phantasying", "fantasying"], "frenetic": ["phrenetic", "phrenetically", "frenetically"], "gibe": ["jibed", "jibes", "gibes", "jibing", "jibe", "gibing", "gibed"], "go": ["goin'", "going", "gos", "went", "gone", "goes"], "good": ["best", "better", "goodliest", "goodish", "goodly", "goodness", "goodlier", "goods"], "hallelujah": ["halleluiah", "hallelujahs", "alleluias", "halleluiahs", "alleluia"], "have": ["'d", "having", "'ave", "hadst", "haved", "had", "'s", "haveing", "hast", "haves", "ve", "'ve", "hath", "has"], "he": ["'e", "'is", "his", "hims", "'im", "him", "hes"], "i": ["me", "mine", "my"], "impanel": ["empanelled", "impaneling", "empanels", "empaneled", "impaneled", "impanelled", "empanelling", "impanels", "impanelling", "empanel", "empaneling"], "inquire": ["enquiringly", "enquires", "enquired", "inquires", "enquire", "enquiring", "inquired", "inquiringly", "inquiring"], "inquirer": ["enquirer", "inquirers", "enquirers"], "inquiry": ["inquiries", "enquiry", "enquiries"], "inure": ["enured", "inures", "enure", "enures", "enuring", "inured", "inuring"], "jail": ["jailed", "gaoling", "gaols", "gaoled", "jails", "jailing", "gaol"], "jailbird": ["gaolbirds", "jailbirds", "gaolbird"], "jailbreak": ["jailbroke", "jailbreaking", "jailbroken", "gaolbreak", "gaolbreaks", "jailbreaks"], "jailer": ["jailers", "jailors", "gaolers", "gaoler", "jailor"], "jeez": ["geez"], "jinn": ["jinns", "jinnis", "jinni", "genies", "djinn", "djinns", "genie"], "karakul": ["caracul"], "ketchup": ["catchups", "catchup", "ketchups", "catsup", "catsups"], "knickknack": ["nicknacks", "knickknacks", "nicknack"], "kumquat": ["kumquats", "cumquats", "cumquat"], "member": ["independents", "members", "membered"], "okay": ["okaying", "okayed", "okays", "mkay"], "old": ["oldest", "eldest", "olde", "older", "oldness", "oldish", "olds", "elder"], "oops": ["whoops"], "opossum": ["possum", "opossums", "possums"], "ow": ["ouch", "yow", "ower", "ows", "yeow"], "phyllo": ["filo"], "rack": ["wracks", "wracked", "wrack", "racking", "racked", "racks", "wracking"], "scallop": ["scollop", "scallops", "escaloping", "escallop", "escallops", "escaloped", "scolloped", "escalloping", "escalloped", "scolloping", "scollops", "scalloped", "escalops", "scalloping", "escalop"], "she": ["hers", "'er", "her", "shes"], "sissy": ["cissier", "cissy", "cissies", "sissies", "cissiest", "sissiest", "sissier"], "they": ["their", "'em", "theys", "them"], "uh-huh": ["mhm", "uh-huhs"], "vial": ["phials", "phial", "vials"], "we": ["wes", "wee", "us", "our"], "well": ["weller", "welling", "best", "wells", "better", "welled", "wellness"], "will": ["willful", "willing", "wilful", "wilfully", "willed", "wilfulness", "willfulness", "wilt", "willfully", "ll", "wills", "'ll", "wo"], "would": ["woulding", "'d", "wouldst"], "it": ["its", "'t"], "badly": ["worse"], "good-humoured": ["better-humoured"], "good-looking": ["best-looking", "better-looking"], "spin-dry": ["dry", "spin-drying", "spun", "spin-dries", "spin-dried"]} -------------------------------------------------------------------------------- /lemmas.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python3 2 | # -*- coding: UTF-8 -*- 3 | 4 | import re 5 | import os 6 | import sys 7 | import json 8 | 9 | from log import log 10 | logger = log(os.path.basename(sys.argv[0])) 11 | 12 | FINAL_LEMMAS_FILE = u'lemmas/lemmas_final.txt' 13 | FINAL_LEMMAS_JOSN_FILE = u'lemmas/lemmas_final.json' 14 | LEMMAS_QS_JSON_FILE = u'lemmas/lemmas_qs.json' 15 | LEMMAS_QS_EXTRA_JSON_FILE = u'lemmas/lemmas_qs_extra.json' 16 | REVERSE_LEMMAS_JSON_FILE = u'lemmas/rev_lemmas.json' 17 | 18 | 19 | """ 20 | 将多个渠道获取的lemmas文件合并，生成最终的 $FINAL_LEMMAS 21 | NOTE: 所有字母均转换为小写 22 | 23 | 为方便比较、合并, 过程中将所有lemmas文件都转换成以下的字典格式，$FINAL_LEMMAS 也是这种格式: 24 | { 25 | 'he': ['his', 'him', 'they'] 26 | } 27 | key是单词 28 | value是list, 内容是 除单词本身 以外的其他形式, 29 | 注：忽略源文件中包含其他属性 30 | 31 | """ 32 | 33 | def parse_lemmas(): 34 | """处理lemmas.txt 下载链接 https://github.com/tsaoye/freeq/blob/master/lemmas.txt 35 | 该文件中,如果单词没有其他形式,则该单词所在行只有一列,即单词本身, 这种单词不会作为结果返回 36 | 单词以tab分隔 37 | 38 | 文件内容示例: 39 | abacus abaci abacuses 40 | 41 | :param filename: 42 | :return: {'message': message, 'result': lemmas_dict} 43 | """ 44 | message = '' 45 | lemmas_dict = {} 46 | try: 47 | with open(u'lemmas/lemmas.txt', 'r', encoding=u'utf-8') as lemmas_file: 48 | for line in lemmas_file.readlines(): 49 | parts = line.split() 50 | word = parts[0].lower() 51 | if len(parts) > 1: 52 | for i in range(1, len(parts)): 53 | if parts[i] and parts[i].lower() != parts[0]: 54 | if not isinstance(lemmas_dict.get(word), list): 55 | lemmas_dict[word] = [] 56 | lemmas_dict[word].append(parts[i].lower()) 57 | 58 | message = 'OK' 59 | except Exception as exc: 60 | import traceback 61 | message = 'parse_lemmas failed, exc:%r, detail: %s' % (exc, traceback.format_exc()) 62 | logger.error(message) 63 | 64 | return {'message': message, 'result': lemmas_dict} 65 | 66 | def parse_bnc_lemmas(): 67 | """处理AntBNC_lemmas_ver_001.txt 68 | 下载链接 http://www.laurenceanthony.net/resources/wordlists/antbnc_lemmas_ver_001.zip 69 | 70 | 文件内容示例: 71 | aaah -> aaahed aaah 72 | 73 | 单词 -> 以tab分隔的各种形式 74 | 75 | :param filename: 76 | :return: {'message': message, 'result': lemmas_dict} 77 | """ 78 | message = '' 79 | lemmas_dict = {} 80 | try: 81 | with open(u'lemmas/AntBNC_lemmas_ver_001.txt', 'r', encoding=u'utf-8') as lemmas_file: 82 | for index, line in enumerate(lemmas_file.readlines()): 83 | parts = line.split() 84 | word = parts[0].lower() 85 | if len(parts) > 2: 86 | for i in range(2, len(parts)): 87 | if parts[i] and parts[i].lower() != parts[0]: 88 | if not isinstance(lemmas_dict.get(word), list): 89 | lemmas_dict[word] = [] 90 | lemmas_dict[word].append(parts[i].lower()) 91 | else: 92 | logger.warning(u'Format is wrong in file AntBNC_lemmas_ver_001.txt, line: %d' % (index + 1)) 93 | message = 'OK' 94 | except Exception as exc: 95 | import traceback 96 | message = 'parse_bnc_lemmas failed, exc:%r, detail: %s' % (exc, traceback.format_exc()) 97 | logger.error(message) 98 | 99 | return {'message': message, 'result': lemmas_dict} 100 | 101 | def parse_e_lemmas(): 102 | """处理e_lemma.txt 103 | 下载链接 http://www.laurenceanthony.net/resources/wordlists/e_lemma.zip 104 | 105 | 文件内容示例: 106 | abandon -> abandons,abandoning,abandoned 107 | 108 | 格式: 109 | 单词/词频 -> 以逗号分隔的该单词的各种形式 110 | 111 | :param filename: 112 | :return: {'message': message, 'result': lemmas_dict} 113 | """ 114 | message = '' 115 | lemmas_dict = {} 116 | try: 117 | with open(u'lemmas/e_lemma.txt', 'r', encoding=u'utf-8') as e_lemmas_file: 118 | for index, line in enumerate(e_lemmas_file.readlines()): 119 | if re.match(r'^[a-zA-Z]+', line): 120 | #if not line.startswith(u'['): 121 | parts = line.split() 122 | word = parts[0].lower() 123 | if len(parts) > 2: 124 | # 文件中的lemmmas是以 , 分隔，故需要再split 125 | final_parts = parts[2].split(u',') 126 | for i in range(0, len(final_parts)): 127 | if final_parts[i] and final_parts[i].lower() != word: 128 | if not isinstance(lemmas_dict.get(word), list): 129 | lemmas_dict[word] = [] 130 | lemmas_dict[word].append(final_parts[i].lower().replace('.', '').replace('!', '')) 131 | else: 132 | logger.warning(u'Format is wrong in file e_lemma.txt, line: %d' % (index + 1)) 133 | message = 'OK' 134 | except Exception as exc: 135 | import traceback 136 | message = 'parse_bnc_lemmas failed, exc:%r, detail: %s' % (exc, traceback.format_exc()) 137 | logger.error(message) 138 | 139 | return {'message': message, 'result': lemmas_dict} 140 | 141 | 142 | def compare_lemmas(base_lemmas, cmp_lemmas): 143 | """以 base_lemmas 为基准，循环每个元素 144 | cmp_lemmas 中与 base_lemmas 单词相同，单词其他形式不同的，结果以 diff_words 返回 145 | 如果 base_lemmas 存在的单词在 cmp_lemmas 中不存在，结果以 not_in_new_lemmas 返回 146 | 147 | 1 如果 'lemmmas' 的value为空，则打印warning log 148 | 2 判断 cmp_lemmas 的单词是否在 base_lemmas 中 149 | 2.1 如果在，则判断cmp_lemmas的key 'lemmas' 的value是否为空 150 | 2.1.1 如果不为空，判断单词的其他形式是否完全相同 151 | 2.1.1.1 如果是，same_count 计数加1 152 | 2.1.1.2 如果不是，将结果记录到diff_words 153 | 2.1.2 如果为空，将 base_lemmas 的元素记录到 not_in_new_lemma 154 | 2.2 如果不在，将 base_lemmas 的元素记录到 not_in_new_lemma 155 | 156 | :param base_lemmas: 157 | :param cmp_lemmas: 158 | :return: 159 | same_count 160 | diff_words 161 | not_in_new_lemmas 162 | """ 163 | same_count = 0 164 | diff_words = {} 165 | not_in_new_lemmas = {} 166 | 167 | for word, lemmas in base_lemmas.items(): 168 | if lemmas: 169 | if word in cmp_lemmas: 170 | new_lemmas = cmp_lemmas.get(word) 171 | if new_lemmas: 172 | lemmas.sort() 173 | new_lemmas.sort() 174 | if lemmas == new_lemmas: 175 | same_count += 1 176 | else: 177 | #diff_lemmas = set(new_lemmas).difference(set(lemmas)) 178 | new_lemmas_set = set(new_lemmas) 179 | lemmas_set = set(lemmas) 180 | diff_lemmas = list(new_lemmas_set - lemmas_set) 181 | if diff_lemmas: 182 | #diff_words.setdefault(word, {'base_lemmmas': lemmas, 'new_lemmas': new_lemmas}) 183 | diff_words.setdefault(word, {'diff_lemmas': diff_lemmas, 'base_lemmmas': lemmas, 'new_lemmas': new_lemmas}) 184 | else: 185 | not_in_new_lemmas.setdefault(word, lemmas) 186 | else: 187 | not_in_new_lemmas.setdefault(word, lemmas) 188 | else: 189 | logger.error(u'word: %s, no lemmas in base_lemmas' % word) 190 | return same_count, diff_words, not_in_new_lemmas 191 | 192 | def merge_lemmas(): 193 | """将各个渠道的lemmas文件合并，FINAL_LEMMAS_FILE 194 | FINAL_LEMMAS_FILE 用于后续生成 JSON格式的文件、快速查询的lemmas 、 reverse lemmas 195 | #2017.09.15 取消生成：每行单词以空格分隔，不会出现只有一个单词的行 196 | :return: 197 | """ 198 | message = 'OK' 199 | lemmas_txt = parse_lemmas() 200 | bnc_lemmas_txt = parse_bnc_lemmas() 201 | e_lemmas_txt = parse_e_lemmas() 202 | 203 | message = lemmas_txt.get(u'message') 204 | if message == 'OK': 205 | lemmas = lemmas_txt.get(u'result') 206 | else: 207 | return message 208 | message = bnc_lemmas_txt.get(u'message') 209 | if message == 'OK': 210 | bnc_lemmas = bnc_lemmas_txt.get(u'result') 211 | else: 212 | return message 213 | message = e_lemmas_txt.get(u'message') 214 | if message == 'OK': 215 | e_lemmas = e_lemmas_txt.get(u'result') 216 | else: 217 | return message 218 | 219 | print(u'lemmas.txt total: %s' % len(lemmas)) 220 | print(u'AntBNC_lemmas_ver_001.txt total: %s' % len(bnc_lemmas)) 221 | print(u'e_lemma.txt total: %s' % len(e_lemmas)) 222 | 223 | lemmas_to_file = {} 224 | for word, lem in lemmas.items(): 225 | new_lem = set() 226 | if isinstance(lem, list): 227 | new_lem.update(lem) 228 | if isinstance(bnc_lemmas.get(word), list): 229 | new_lem.update(bnc_lemmas.get(word)) 230 | if isinstance(e_lemmas.get(word), list): 231 | new_lem.update(e_lemmas.get(word)) 232 | final_lem = [] 233 | for le in new_lem: 234 | if len(le) != 1: 235 | final_lem.append(le) 236 | lemmas_to_file.setdefault(word, final_lem) 237 | 238 | for word, lem in bnc_lemmas.items(): 239 | new_lem = set() 240 | if isinstance(lem, list): 241 | new_lem = set(lem) 242 | if isinstance(lemmas.get(word), list): 243 | new_lem.update(lemmas.get(word)) 244 | if isinstance(e_lemmas.get(word), list): 245 | new_lem.update(e_lemmas.get(word)) 246 | final_lem = [] 247 | for le in new_lem: 248 | if len(le) != 1: 249 | final_lem.append(le) 250 | lemmas_to_file.setdefault(word, final_lem) 251 | 252 | for word, lem in e_lemmas.items(): 253 | new_lem = set() 254 | if isinstance(lem, list): 255 | new_lem = set(lem) 256 | if isinstance(lemmas.get(word), list): 257 | new_lem.update(lemmas.get(word)) 258 | if isinstance(bnc_lemmas.get(word), list): 259 | new_lem.update(e_lemmas.get(word)) 260 | final_lem = [] 261 | for le in new_lem: 262 | if len(le) != 1: 263 | final_lem.append(le) 264 | lemmas_to_file.setdefault(word, final_lem) 265 | # write to file 266 | with open(FINAL_LEMMAS_FILE, 'w', encoding=u'utf-8') as lemmas_new: 267 | for word, value in lemmas_to_file.items(): 268 | lemmas_new.write(word + u' -> ' + ' '.join(value) + '\n') 269 | with open(FINAL_LEMMAS_JOSN_FILE, 'w', encoding=u'utf-8') as lemmas_json: 270 | lemmas_json.write(json.dumps(lemmas_to_file)) 271 | return message 272 | 273 | def create_reverse_lemmas(): 274 | """创建reverse lemmas 275 | :return: 276 | """ 277 | message = u'' 278 | rev_lemmas_dict = {} 279 | try: 280 | with open(FINAL_LEMMAS_FILE, 'r', encoding=u'utf-8') as lemmas_file: 281 | lemmas_file.readlines() 282 | 283 | with open(FINAL_LEMMAS_JOSN_FILE, u'r', encoding=u'utf-8') as lemmas_file: 284 | final_lemmas = json.loads(lemmas_file.read()) 285 | for word, lemmas in final_lemmas.items(): 286 | for i in range(0, len(lemmas)): 287 | rev_lemmas_dict[lemmas[i]] = word 288 | with open(REVERSE_LEMMAS_JSON_FILE, u'w', encoding=u'utf-8') as rev_lemmas_json: 289 | rev_lemmas_json.write(json.dumps(rev_lemmas_dict)) 290 | message = u'OK' 291 | except Exception as exc: 292 | import traceback 293 | message = 'FAILED: create_lemmas_dict, exc:%r, detail: %s' % (exc, traceback.format_exc()) 294 | logger.error(message) 295 | 296 | return message 297 | 298 | def create_quick_search_lemmas(): 299 | """将lemmas文件划分成以下两个文件，用于快速搜索： 300 | lemmas_qs_extra.txt 对lemmas_quick_search.txt 的补充：使用字典存储，任何一个lemma与单词的开头字母不同，存到这个文件中 301 | { 302 | 'I': ['me'], 303 | } 304 | lemmas_qs.txt 二级字典格式存储，第一级key是26个字母，value也是字典 305 | { 306 | 'a': { 307 | 'a': ['an'], 308 | 'abacus': ['abaci', 'abacuses'], 309 | ... 310 | } 311 | 'b': {...} 312 | ... 313 | 'z': {...} 314 | } 315 | 316 | :return: 317 | """ 318 | lemmas_qs = {} 319 | lemmas_qs_extra = {} 320 | message = u'' 321 | try: 322 | with open(FINAL_LEMMAS_JOSN_FILE, 'r', encoding=u'utf-8') as lemmas_file: 323 | final_lemmas = json.loads(lemmas_file.read()) 324 | for word, lemmas in final_lemmas.items(): 325 | for i in range(0, len(lemmas)): 326 | if lemmas[i][0] != word[0]: 327 | lemmas_qs_extra.setdefault(word, lemmas) 328 | break 329 | if not lemmas_qs_extra.get(word): 330 | alpha_lemmas = lemmas_qs.setdefault(word[0], {}) 331 | alpha_lemmas[word] = lemmas 332 | 333 | with open(LEMMAS_QS_JSON_FILE, 'w', encoding=u'utf-8') as lemmas_qs_file: 334 | lemmas_qs_file.write(json.dumps(lemmas_qs)) 335 | with open(LEMMAS_QS_EXTRA_JSON_FILE, 'w', encoding=u'utf-8') as lemmas_qs_extra_file: 336 | lemmas_qs_extra_file.write(json.dumps(lemmas_qs_extra)) 337 | message = u'OK' 338 | except Exception as exc: 339 | import traceback 340 | message = 'FAILED: create_quick_search, exc:%r, detail: %s' % (exc, traceback.format_exc()) 341 | logger.error(message) 342 | return message 343 | 344 | def test_lemmas_qs(): 345 | lemmas_qs_all = {} 346 | with open(FINAL_LEMMAS_JOSN_FILE, 'r', encoding=u'utf-8') as lemmas_final_json_file: 347 | lemmas_final_json = json.loads(lemmas_final_json_file.read()) 348 | with open(LEMMAS_QS_JSON_FILE, 'r', encoding=u'utf-8') as lemmas_qs_file: 349 | lemmas_qs = json.loads(lemmas_qs_file.read()) 350 | with open(LEMMAS_QS_EXTRA_JSON_FILE, 'r', encoding=u'utf-8') as lemmas_qs_extra_file: 351 | lemmas_qs_extra = json.loads(lemmas_qs_extra_file.read()) 352 | 353 | for alpha_lemmas in lemmas_qs.values(): 354 | lemmas_qs_all.update(alpha_lemmas) 355 | lemmas_qs_all.update(lemmas_qs_extra) 356 | 357 | import operator 358 | if not operator.eq(lemmas_qs_all, lemmas_final_json): 359 | logger.error(u'lemmas_qs != lemmas_final') 360 | if lemmas_qs_all.keys() != lemmas_final_json.keys(): 361 | print(u'key !=') 362 | else: 363 | for key, value in lemmas_qs_all.items(): 364 | if value != lemmas_final_json.get(key): 365 | print(u'FAILED: %s, %s' % (value, lemmas_final_json.get(key))) 366 | 367 | def create_lemmas_file(force_create=False): 368 | """生成各种lemmas文件。 369 | 默认只有文件不存在的时候才生成， 370 | 也可以指定force_create 为True，强制生产 371 | """ 372 | message = u'OK' 373 | if force_create: 374 | message = test_and_create() 375 | else: 376 | if not os.path.isfile(FINAL_LEMMAS_FILE) or not os.path.isfile(FINAL_LEMMAS_JOSN_FILE): 377 | merge_lemmas() 378 | if not os.path.isfile(LEMMAS_QS_JSON_FILE) or not os.path.isfile(LEMMAS_QS_EXTRA_JSON_FILE): 379 | create_quick_search_lemmas() 380 | if not os.path.isfile(REVERSE_LEMMAS_JSON_FILE): 381 | create_reverse_lemmas() 382 | 383 | def test_and_create(): 384 | message1 = merge_lemmas() 385 | if message1 == 'OK': 386 | message2 = create_quick_search_lemmas() 387 | if message2 == "OK": 388 | test_lemmas_qs() 389 | else: 390 | logger.error(message2) 391 | message3 = create_reverse_lemmas() 392 | if message3 != "OK": 393 | logger.error(message3) 394 | else: 395 | logger.error(message1) 396 | 397 | def main(): 398 | test_and_create() 399 | 400 | if __name__ == '__main__': 401 | main() 402 | -------------------------------------------------------------------------------- /all_text_trans.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python3 2 | # -*- coding: UTF-8 -*- 3 | 4 | import re 5 | import json 6 | import os 7 | import sys 8 | import argparse 9 | import collections 10 | import csv 11 | import traceback 12 | 13 | from log import log 14 | logger = log(os.path.basename(sys.argv[0])) 15 | 16 | LEMMAS_QS_JSON_FILE = u'lemmas/lemmas_qs.json' 17 | LEMMAS_QS_EXTRA_JSON_FILE = u'lemmas/lemmas_qs_extra.json' 18 | REVERSE_LEMMAS_JSON_FILE = u'lemmas/rev_lemmas.json' 19 | 20 | class AllText(object): 21 | def __init__(self, file_path): 22 | """ 23 | :param file_path: 24 | 25 | 生成以下私有变量： 26 | __file_path string, 输入的文件名 27 | __all_text string. 全文 28 | 29 | __words list, 未去重的所有单词 30 | __words_cnt dict, 根据原始文本统计的词频，未作任何筛除{word: frequency, ...} 31 | __words_distinct set, 去重后的所有单词，包含了各种形式 32 | 33 | # 单词均为小写 34 | __lower_word list, 35 | __lower_words_cnt dict 36 | __lower_words_distinct set 37 | 38 | """ 39 | try: 40 | with open(file_path, u'r', encoding=u'utf-8') as allText_file: 41 | self.__file_path = file_path 42 | # 将中文 单引号、双引号 替换为 相应的英文标点 43 | self.__all_text = allText_file.read().replace(u"‘", u"'").replace(u'’', u"'").\ 44 | replace(u'“', u'"').replace(u'”', u'"') 45 | 46 | self.__words = self.parse_text(self.__all_text) 47 | self.__words_cnt = dict(collections.Counter(self.__words)) 48 | self.__words_distinct = set(self.__words) 49 | 50 | self.__lower_word = [word.lower() for word in self.__words] 51 | self.__lower_words_cnt = dict(collections.Counter(self.__lower_word)) 52 | self.__lower_words_distinct = set(self.__lower_word) 53 | except Exception as exc: 54 | message = u'FAILED: Open "%s", exc:%r, detail: %s' % (file_path, exc, traceback.format_exc()) 55 | logger.error(message) 56 | 57 | def parse_text(self, text): 58 | """根据输入的文本，返回解析后的单词列表 59 | 处理以下缩写： 60 | n't 's 're 'd 've 'll 61 | 中文单引号、双引号 62 | :param text: 63 | :return: 64 | """ 65 | # 先将标点符号替换为空格，除了 单引号。因为 部分单词的缩写会使用单引号，在后面进行处理 66 | temp_words = text.replace(u'.', u' ').replace(u'?', u' ').replace('!', ' ').replace(u';', u' ').\ 67 | replace(u',', u' ').replace(u':', u' ').replace(u'(', u' ').replace(u')', u' ').replace(u'{', u' '). \ 68 | replace(u'}', u' ').replace(u'[', u' ').replace(u']', u' ').replace(u'_', u' ').replace(u'"', u' ').\ 69 | replace(u'-\r\n', u'-').replace(u'-\n', u'-').replace('—', ' ').replace('--', ' ').split() 70 | short_words = [u"n't", u"'re", u"'s", u"'d", u"'ve", u"'ll"] 71 | words = [] 72 | for w in temp_words: 73 | w = w.replace(u' ', u'') 74 | if w: 75 | is_short = False 76 | for s_w in short_words: 77 | if w.endswith(s_w): 78 | words.extend(self.__parse_no_short_words(w.split(s_w)[0])) 79 | words.append(s_w) 80 | is_short = True 81 | break 82 | if not is_short: 83 | words.extend(self.__parse_no_short_words(w)) 84 | return words 85 | 86 | def __parse_no_short_words(self, text): 87 | """处理不含缩写的文本，返回解析后的单词列表 88 | 前面已经替换了除 '—' '--' "'" "’" 的标点 89 | :param text: 90 | :return: 91 | """ 92 | words = [] 93 | if text and text[0].isalpha(): 94 | if text[-1] == '-': 95 | text = text[:-1] 96 | 97 | # 去除 非 字母和- 的字符 98 | words.append(''.join([x for x in text if x.isalpha() or x == '-'])) 99 | return words 100 | 101 | def get_all_text(self): 102 | return self.__all_text 103 | 104 | def get_words_list(self): 105 | return self.__words 106 | 107 | def get_words_count(self): 108 | """ 109 | :return: dict 110 | """ 111 | return self.__words_cnt 112 | 113 | def get_words_distinct(self): 114 | """ 115 | :return: set 116 | """ 117 | return self.__words_distinct 118 | 119 | def get_lower_words(self): 120 | return self.__lower_words 121 | 122 | def get_lower_word_cnt(self): 123 | """ 124 | :return: dict 125 | """ 126 | return self.__lower_words_cnt 127 | 128 | def get_lower_words_distinct(self): 129 | """ 130 | :return: set 131 | """ 132 | return self.__lower_words_distinct 133 | 134 | def get_del_lemma_words(self): 135 | """使用文本的小写单词列表 136 | 如果遇到异常，返回 原文单词列表 (or 终止 程序?) 137 | :return: 单词均为小写 138 | """ 139 | from lemmas import create_lemmas_file 140 | create_lemmas_file() 141 | try: 142 | with open(LEMMAS_QS_JSON_FILE, u'r', encoding=u'utf-8') as lemmas_qs_file: 143 | self.__lemmas_qs = json.loads(lemmas_qs_file.read()) 144 | with open(LEMMAS_QS_EXTRA_JSON_FILE, u'r', encoding=u'utf-8') as lemmas_qs_extra_file: 145 | self.__lemmas_qs_extra = json.loads(lemmas_qs_extra_file.read()) 146 | return [self.get_base_word(word) for word in self.__lower_word] 147 | except Exception as exc: 148 | message = u'FAILED: handle lemmms_qs.json/lemmas_qs_extra.json, exc:%r, detail: %s' % ( 149 | exc, traceback.format_exc()) 150 | logger.error(message) 151 | return self.__lower_word 152 | 153 | def get_del_lemma_words_cnt(self): 154 | """单词均为小写 155 | :return: dict 156 | """ 157 | return dict(collections.Counter(self.get_del_lemma_words())) 158 | 159 | def get_del_lemma_words_distinct(self): 160 | """单词均为小写 161 | :return: set 162 | """ 163 | return set(self.get_del_lemma_words()) 164 | 165 | def get_base_word(self, word): 166 | """返回单词的原形，如果lemmas表中没有找到，则返回该单词本身 167 | :param word: 168 | :return: 169 | """ 170 | if word: 171 | alpha_lemmas = self.__lemmas_qs.get(word[0]) 172 | if word in alpha_lemmas: 173 | # word is base_word 174 | return word 175 | else: 176 | for base_word, a_lemmas in alpha_lemmas.items(): 177 | if a_lemmas and (word in a_lemmas): 178 | return base_word 179 | for base_word, a_lemmas in self.__lemmas_qs_extra.items(): 180 | if word in a_lemmas: 181 | return base_word 182 | return word 183 | 184 | def get_hard_words(self, frequency=0, vocabulary=u'', del_lemmas=True): 185 | """根据去重后的原型单词列表，返回难词表 186 | :return: set 187 | """ 188 | if frequency > 0: 189 | return self.del_by_frq(frequency, del_lemmas=del_lemmas) 190 | elif vocabulary: 191 | return self.del_by_vocab(vocabulary, del_lemmas=del_lemmas) 192 | else: 193 | return set() 194 | 195 | def del_by_frq(self, frequency=0, del_lemmas=True): 196 | """ 197 | 如果 del_lemmas 为True： 198 | 以去重、去lemmas的 __del_lemma_words_distinct 为基准 199 | 否则： 200 | 将文本中去重后的小写单词作为基准 201 | 再：根据词频表 移除单词、根据简单词表 移除单词 202 | 203 | NOTE: windows下文件另存为utf8，注意另存为 utf-8 无BOM头 格式，否则Windows会在文件开始处添加BOM头EF BB 204 | :param frequency: int 205 | :return: set 206 | """ 207 | if frequency > 0: 208 | del_lemma_words_distinct = self.get_del_lemma_words_distinct() 209 | if del_lemmas: 210 | hard_words = del_lemma_words_distinct 211 | else: 212 | hard_words = self.__lower_words_distinct 213 | simple_words = self.__load_simple_words() 214 | with open(u'vocabulary/COCA60000.txt', u'r', encoding=u'utf-8') as coca: 215 | coca_list = coca.readlines() 216 | for word in coca_list[:frequency]: 217 | word = word.lower().strip() 218 | if word in del_lemma_words_distinct: 219 | if word in hard_words: 220 | hard_words.remove(word) 221 | for word in simple_words: 222 | if word in del_lemma_words_distinct: 223 | if word in hard_words: 224 | hard_words.remove(word) 225 | return hard_words 226 | 227 | def del_by_vocab(self, vocabulary=u'', del_lemmas=True): 228 | """ 229 | :param vocabulary: 230 | :return: set 231 | """ 232 | simple_words = self.__load_simple_words() 233 | del_lemma_words_distinct = self.get_del_lemma_words_distinct() 234 | hard_words = set() 235 | 236 | if del_lemmas: 237 | hard_words = set(del_lemma_words_distinct).difference(simple_words) 238 | else: 239 | hard_words = set(self.__lower_words_distinct).difference(simple_words) 240 | if vocabulary == u'HIGHSCHOOL': 241 | high_school_words = self.__load_high_school_words() 242 | hard_words = hard_words.difference(high_school_words) 243 | elif vocabulary == u'CET4': 244 | pass 245 | elif vocabulary == u'CET6': 246 | hard_words = hard_words.difference(self.__load_high_school_words()) 247 | hard_words = hard_words.difference(self.__load_cet6_words()) 248 | elif vocabulary == u'IELTS': 249 | pass 250 | elif vocabulary == u'TOEFL': 251 | hard_words = hard_words.difference(self.__load_high_school_words()) 252 | hard_words = hard_words.difference(self.__load_cet6_words()) 253 | hard_words = hard_words.difference(self.__load_toefl_words()) 254 | elif vocabulary == u'GRE': 255 | hard_words = hard_words.difference(self.__load_high_school_words()) 256 | hard_words = hard_words.difference(self.__load_cet6_words()) 257 | hard_words = hard_words.difference(self.__load_toefl_words()) 258 | hard_words = hard_words.difference(self.__load_gre_words()) 259 | return hard_words 260 | 261 | def __load_simple_words(self): 262 | """加载 简单词 表 263 | :return: simple_words, set 264 | """ 265 | simple_words = set() 266 | try: 267 | with open(u'vocabulary/simple_words.txt', u'r', encoding=u'utf-8') as simple: 268 | for w in simple.readlines(): 269 | if not w.startswith('#'): 270 | simple_words.add(w.strip().lower()) 271 | except Exception as exc: 272 | message = u'FAILED: handle FILE simple_words, exc:%r, detail: %s' % (exc, traceback.format_exc()) 273 | logger.error(message) 274 | return simple_words 275 | 276 | def __load_high_school_words(self): 277 | """加载 HIGHSCHOOL 词汇表 278 | :return: high_school_words, set 279 | """ 280 | high_school_words = set() 281 | try: 282 | with open(u'vocabulary/highschool_edited.txt', u'r', encoding=u'utf-8') as high_school: 283 | for w in high_school.readlines(): 284 | high_school_words.add(w.strip().lower()) 285 | except Exception as exc: 286 | message = u'FAILED: handle File high school, exc:%r, detail: %s' % (exc, traceback.format_exc()) 287 | logger.error(message) 288 | return high_school_words 289 | 290 | def __load_cet6_words(self): 291 | """加载 CET6 词汇表 292 | :return: cet6_words, set 293 | """ 294 | cet6_words = set() 295 | try: 296 | with open(u'vocabulary/CET6_edited.txt', u'r', encoding=u'utf-8') as cet6: 297 | for w in cet6.readlines(): 298 | cet6_words.add(w.strip().lower()) 299 | except Exception as exc: 300 | message = u'FAILED: handle File cet6, exc:%r, detail: %s' % (exc, traceback.format_exc()) 301 | logger.error(message) 302 | return cet6_words 303 | 304 | def __load_toefl_words(self): 305 | """加载 TOEFL 词汇表 306 | :return: toefl_words, set 307 | """ 308 | toefl_words = set() 309 | try: 310 | with open(u'vocabulary/WordList_TOEFL.txt', u'r', encoding=u'utf-8') as toefl: 311 | for w in toefl.readlines(): 312 | toefl_words.add(w.strip().lower()) 313 | except Exception as exc: 314 | message = u'FAILED: handle File TOEFL, exc:%r, detail: %s' % (exc, traceback.format_exc()) 315 | logger.error(message) 316 | return toefl_words 317 | 318 | def __load_gre_words(self): 319 | """加载 GRE 词汇表 320 | :return: gre_words, set 321 | """ 322 | gre_words = set() 323 | try: 324 | with open(u'vocabulary/WordList_GRE.txt', u'r', encoding=u'utf-8') as gre: 325 | for w in gre.readlines(): 326 | gre_words.add(w.strip().lower()) 327 | except Exception as exc: 328 | message = u'FAILED: handle File GRE, exc:%r, detail: %s' % (exc, traceback.format_exc()) 329 | logger.error(message) 330 | return gre_words 331 | 332 | def get_translated(self, words=[], frequency=0, vocabulary=u''): 333 | """ 334 | :param words: 用户指定要翻译的单词表 335 | :param frequency: 指定剔除 词频在frequency以内的单词 336 | :param vocabulary: 指定 提出 某个词汇表，支持 'CET4' 'CET6' 'TOEFL' 'GRE' 337 | :return: 338 | """ 339 | hard_words = set(words) 340 | if frequency: 341 | hard_words = hard_words.union(self.del_by_frq(frequency=frequency)) 342 | elif vocabulary: 343 | hard_words = hard_words.union(self.del_by_vocab(vocabulary=vocabulary)) 344 | if hard_words: 345 | words_trans = self.get_words_tans(hard_words) 346 | else: 347 | words_trans = self.get_words_tans(self.__words_distinct) 348 | all_text_trans = self.__all_text 349 | for w, tran in words_trans.items(): 350 | if tran: 351 | # 替换之外，给生词和翻译加上html格式 352 | w_tran = u'' + w + '' + '(' + tran + ')' 353 | # 为避免误替换单词，被替换的单词前后必须有以下标点的任意一个: 354 | # 空格,:.'"?!@;()\r\n 355 | # NOTE: 如果一个单词连续出现两次以上，则只会替换第一个 356 | pattern = re.compile(r'([ ,:\'\"\.\?!@;(\n])%s([ ,:\'\"\.\?!@;)\n])' % w) 357 | all_text_trans = pattern.sub(r'\1%s\2' % w_tran, all_text_trans) 358 | 359 | all_text_trans = all_text_trans.replace(u'\n', u'
') 360 | 361 | with open(self.__file_path + u'-trans.html', u'w', encoding=u'utf-8') as fout: 362 | fout.write(all_text_trans) 363 | 364 | def __load_dictionary(self): 365 | """加载 英汉词典 366 | :return: 367 | """ 368 | dictionary = {} 369 | #with open(u'dictionary/vivo_edited.csv', u'r', encoding=u'utf-8') as ec: 370 | with open(u'dictionary/简明英汉词典（vivo_edited）.csv', u'r') as ec: 371 | f_ecdict = csv.reader(ec) 372 | headers = next(f_ecdict) 373 | for row in f_ecdict: 374 | # NOTE: 将词典的 key转换为小写 375 | dictionary[row[0].lower()] = row[1] 376 | return dictionary 377 | 378 | def __get_orignal_words(self, words): 379 | """根据 words 中传入的单词，查找原文的单词 380 | :param words: list 381 | :return: 382 | """ 383 | orignal_words = set() 384 | for w in self.__words: 385 | if w in words: 386 | orignal_words.add(w) 387 | elif w.lower() in words: 388 | orignal_words.add(w) 389 | return orignal_words 390 | 391 | def get_words_tans(self, words=[]): 392 | """如果单词没有解释，则查询其是否有原型单词，如有，使用其原型单词的解释 393 | :param words: 394 | :param dictionary: 395 | :return: 396 | """ 397 | dictionary = self.__load_dictionary() 398 | words = self.__get_orignal_words(words) 399 | words_trans = {} 400 | get_lemmas_dict = self.__get_rev_lemmas() 401 | if get_lemmas_dict.get(u'message') == u'OK': 402 | rev_lemmas_dict = get_lemmas_dict.get(u'rev_lemmas_dict') 403 | for w in words: 404 | lower_w = w.lower() 405 | trans = dictionary.get(lower_w) 406 | if trans: 407 | words_trans[w] = dictionary.get(lower_w) 408 | else: 409 | org_word = rev_lemmas_dict.get(lower_w) 410 | if not org_word: 411 | logger.warning(u'%s, No translation' % w) 412 | else: 413 | org_trans = dictionary.get(org_word.lower()) 414 | if not org_trans: 415 | logger.warning(u'%s, No translation' % w) 416 | else: 417 | words_trans[w] = org_trans 418 | return words_trans 419 | 420 | def __get_rev_lemmas(self): 421 | """ 422 | :return: 423 | """ 424 | rev_lemmas_dict = {} 425 | message = u'' 426 | try: 427 | with open(REVERSE_LEMMAS_JSON_FILE, u'r', encoding=u'utf-8') as rev_lemmas_dict_file: 428 | rev_lemmas_dict = json.loads(rev_lemmas_dict_file.read()) 429 | message = u'OK' 430 | except Exception as exc: 431 | import traceback 432 | message = u'FAILED: handle rev_lemmas_dict.json, exc:%r, detail: %s' % (exc, traceback.format_exc()) 433 | logger.error(message) 434 | return {u'message': message, u'rev_lemmas_dict': rev_lemmas_dict} 435 | 436 | def main(): 437 | import time 438 | print(time.strftime("%Y-%m-%d %H:%M:%S", time.localtime()) + ", begin to init") 439 | #allText = AllText(u'tests/ShortHistory.txt') 440 | #allText = AllText(u'tests/1342-0.txt') 441 | allText = AllText(u'tests/pg1260.txt') 442 | #allText = AllText(u'tests/driveless.txt') 443 | #allText = AllText(u'tests/test.txt') 444 | print(time.strftime("%Y-%m-%d %H:%M:%S", time.localtime()) + ", start to get hard words") 445 | #hard_words = allText.get_hard_words(count=5000) 446 | hard_words = allText.get_hard_words(vocabulary=u'HIGHSCHOOL') 447 | print(time.strftime("%Y-%m-%d %H:%M:%S", time.localtime()) + ", deleted HIGHSCHOOL: " + str(len(hard_words))) 448 | hard_words = allText.get_hard_words(vocabulary=u'CET6') 449 | print(time.strftime("%Y-%m-%d %H:%M:%S", time.localtime()) + ", deleted CET6: " + str(len(hard_words))) 450 | hard_words = allText.get_hard_words(vocabulary=u'TOEFL') 451 | print(time.strftime("%Y-%m-%d %H:%M:%S", time.localtime()) + ", deleted TOEFL: " + str(len(hard_words))) 452 | hard_words = allText.get_hard_words(vocabulary=u'GRE') 453 | print(time.strftime("%Y-%m-%d %H:%M:%S", time.localtime()) + ", deleted GRE: " + str(len(hard_words))) 454 | print(hard_words) 455 | 456 | print(time.strftime("%Y-%m-%d %H:%M:%S", time.localtime()) + ", begin to transalte") 457 | #trans_allText = allText.get_translated() 458 | #trans_allText = allText.get_translated(vocabulary=u'CET6') 459 | trans_allText = allText.get_translated(vocabulary=u'GRE') 460 | print(time.strftime("%Y-%m-%d %H:%M:%S", time.localtime()) + ", wrote to file") 461 | 462 | if __name__ == '__main__': 463 | main() -------------------------------------------------------------------------------- /tests/driveless.txt: -------------------------------------------------------------------------------- 1 | The Driverless Economy 2 | Why robocars will be a bigger deal than you probably imagine 3 | 4 | Concept art of a city of the future. 5 | Since Google announced its first self-driving car prototypes back in 2012, people have been speculating how new technologies, AI, and automation may impact the global economy. Heck, some have even written entire books on this! 6 | It’s hard not to spend some time wondering what’s going to happen to the millions of truck and taxi drivers that are now roaming the streets, once this technological wonder becomes available to the masses. Will they all be out of a job? How are we to cope with such an event, especially at a time where governments are not exactly floating in cash — most are plagued by crippling national debt. 7 | I have long suggested that we should explore and experiment with Universal Basic Income as a means to potentially restructure our society and overcome the looming problem of technological unemployment. I am not alone in thinking this. Over the years, Silicon Valley billionaires and internet entrepreneurs like Elon Musk, Sam Altman, and Mark Zuckerberg have taken a similar position. 8 | When I first started giving lectures on this in 2012, audiences were split 90–10. The majority believed technological unemployment was a non-issue, which we could easily solve like we did with the past industrial revolutions. 9 | Today, the ratio is still 90–10, but reversed. I have a hard time finding techno-skeptics. 10 | However, while many have come around when it comes to the systematic loss of jobs, I find that: 11 | Most people don’t have a full grasp of the profound implications that an automated economy is going to bring. To explain it, perhaps there is no better and clearer example than autonomous vehicles. 12 | 13 | One of the first prototypes of self-driving car developed by Google. 14 | I will attempt to show how a single innovation is going to completely disrupt not one, but several industries, with non-obvious rippling effects in almost all facets of our daily lives. 15 | Note: for the sake of brevity, I’ll start referring to self-driving cars and the more general concept of autonomous vehicle — which includes cars, trucks, taxies, buses, vans, pods, trains, drones, and whatever comes next — as robocars. It’s shorter and gets the point across faster. 16 | Also, I think it sounds cooler. 17 | 90% safer 18 | Robocars are filled with sensors. Different manufacturers use a combination of cameras, lasers, infrared, RADAR, LiDAR, ultrasonic, wheel speed sensors, passive visual, sonar, GPS, accelerometer, gyroscope, temperature, humidity, and a bunch more. 19 | Robocars can see and move around in rain, fog, and at night with no lights on. They can sense other robocars. They don’t need traffic lights. They can see 360° and be aware at all time of things we won’t ever be able to see. 20 | 21 | Nearly 1.3 million people die in road crashes each year, an average of 3,287 deaths a day. An additional 20–50 million are injured or disabled. 22 | Every. Single. Year. 23 | Robocars can easily bring that number down 90%, and in the long run I predict over 99% — a factor of 100. The reduction of car accidents brought by the introduction of fully autonomous will be even more dramatic and sudden than that of airplanes. 24 | 25 | In time, I predict that driving will become illegal, and be relegated to specific areas for driving enthusiasts. Just like hunting. A sport for the few. 26 | Robocars won’t even be built with a steering wheel, because it will be an obsolete piece of design that won’t serve any purpose. They will be redesigned from scratch, allowing us to do a variety of things inside, and become more like an extension of our homes. 27 | 90% cheaper 28 | The biggest costs of cars are gas, insurance, maintenance, and—when you take a taxi—the driver. 29 | Robocars bring all of these costs down to practically zero. 30 | With 90% fewer accidents, insurance costs will drop 90% or more. 31 | Autonomous driving means you don’t need to pay for a human being behind the wheel, which is by far the biggest cost when one takes a taxi. 32 | Robocars will be electric, and if you create your own electricity using solar PV or other methods, the cost of taking a trip will be practically zero. Electricity generation from solar is now cheaper than coal and gas in many countries, and this trend will continue in the future, bringing the cost down exponentially. 33 | 34 | Credit: Ramez Naam. 35 | Electric cars are still not mainstream due to mainly two reasons: range and cost. Both are improving exponentially, thanks to better battery technology and car design. 36 | Robocars can leapfrog and overcome these obstacles much faster than normal electric cars would. An organized fleet of robocars can be incredibly efficient at organizing routes and switches. You will not need to plan for long trips, look at the available charging stations along the way, or remember to charge your car during the night. 37 | Robocars can do that automatically, so that you never have to think about it. Whether you have to do 10km or 10,000km, it doesn’t matter. It’s just something you will never be concerned with. A robocar will know which station to approach and at which time, it will come close to the appropriate replacement robocar whenever needed and open both doors for you. All you have to do is get out of one robocars and hop in the other every few hundred km. All in all, the process will take about 5 seconds, much less than would you’d need with any gasoline car and its refueling requirements. 38 | Electric cars are also more reliable and require less maintenance. They are much simpler, have fewer parts, and can do most of their diagnostics via software, which can be regularly updated and controlled remotely. 39 | Robocars don’t have to wait for electric vehicles to match the range of gasoline cars, or to even become as cheap. 40 | The combined benefits of better logistics and reduced maintenance can make cost and range anxiety obsolete problem. 41 | More flexible, more personalized 42 | Robocars will also adjust to the needs of the individual person. It’s estimated that most cars move around with a single person inside, a huge waste of space and resources. 43 | The average midsize car weighs 1,590 kilograms. The average human is 63kg. We essentially use a huge amount of energy to accelerate incredibly heavy objects, which take large amounts of space and weigh more than 20 times what they should be carrying. 44 | That’s insane. 45 | Aside from the failure to properly organize logistics with car pooling, the biggest issue is that we buy cars that are one-size fits all. We expect to be able to use them in cities, mountains, to bring kids to school, to go to work by ourselves, and to venture out for a weekend at the lake with friends. Also, we want to feel safe, so we buy increasingly bigger and more inefficient cars. 46 | This creates a vicious cycle. 47 | With robocars, we don’t need to buy one that fits all sizes and uses. We can just order the robocar we need, when we need it, at a fraction of the cost. 48 | For a trip alone, we could use single-occupant robocars, which are incredibly efficient and can zip around with ease anywhere in the city. 49 | 50 | Concept car by Toyota for a single occupant robocar. 51 | If we’re with a group of friends, we could share a small van. 52 | 53 | And if we like to travel in luxury, we can do that as well. 54 | 55 | In other words: 56 | Robocars can be much cheaper, reliable, flexible, and comfortable than regulars cars, even if they have higher upfront costs and can’t drive as far. 57 | They are better in every regard from day one. 58 | A lot better. 59 | 90% faster 60 | One of the main reason robocars are not already roaming the streets is the same reason we have traffic jams. Streets are filled with stupid, reckless humans at the wheel of coffin-shaped metal boxes, whizzing about inefficiently and dangerously. 61 | 62 | We can’t even seem to manage to follow the simplest of rules, and we constantly cause unnecessary traffic jams and accidents. If we simply kept the proper distance between cars, we could get rid of phantom traffic jams, which are infesting our highways. But humans are generally stupid when it comes to driving, and half of us are filled with testosterone, so we don’t. 63 | A new study out of the University of Illinois at Urbana-Champaign suggests that the addition of just a small number of autonomous cars can ease the congestion on our roads. The presence of just one autonomous car reduces the standard deviation in speed of all the cars in the jam by around 50 percent, or a factor of 2. 64 | 65 | In this replication of a phantom traffic jam, just a single car with limited autonomy (the silver SUV) is enough to clear up congestion involving 20 other cars. (source) 66 | If all cars were autonomous, they could effectively communicate with each other, eliminate almost 100% of traffic jams, and always cruise at full speed, thus reducing commuting time by a factor of 10 at the very least. 67 | 90% fewer cars 68 | Robocar rides will be cheap and ubiquitous. Unless you live in a remote, rural area, you will never need to buy a car. You might want to, but you won’t need to. 69 | In cities and towns, fleets of robocars will be constantly moving around, ready to pick up anyone in need. And because they are connected and can talk to each other at the speed of light, they can organize logistics much better than we ever can or will. 70 | 71 | A variety of advanced algorithms will optimize the number of cars required, predict flows, incoming demand, be linked to weather stations and sensors on the roads. IoT, Big Data, and Deep Learning will play a huge role in this, feeding each other in a virtuous cycle. More cheap and ubiquitous sensors around the cities means more and better quality data, which can train the algorithms to become more accurate. 72 | In time, people will realize that owning a car in cities is not a good thing, but a drag. The younger generation already realized this, and it’s relying more on car sharing and public transport. Robocars offer all the pros, and none of the cons. They will blow every other means of transportation out of the water. 73 | 90% greener 74 | This is a no brainer. With 90% fewer cars on the road, it follows that there will be a significant reduction in emissions as well. 75 | However, the relationship might not be proportional. Because cars spend most of their time idle doing nothing, they’re wasting valuable space, but they’re not emitting anything while sitting there. Robocars, on the other hand, would be constantly moving people around to optimize use. This would increase emissions per car, but not per capita, it would roughly stay the same. 76 | Now let’s factor in the other variables. Because of the dramatic decrease in traffic, congestions, and the optimized routes, we should expect an improvement of at least 4x, perhaps even 10x. 77 | But we haven’t even scratched the surface. 78 | The biggest reduction in emissions will come from the switch to electric vehicles. 79 | Electric cars have virtually zero emissions, both of CO2 or harmful pollution, unless then one used to create them (which is more or less equivalent to that of regular cars). 80 | The combined effect of better logistics, the elimination of traffic jams and the switch to electric will bring emissions down 10–1000x. 81 | 90% fewer parking spaces, 27% more free land area 82 | It’s estimated that in the US there are 8 parking spots for every car, covering up to 30% of our cities. Just let that sink in. Eight parking spaces for every single vehicle. 83 | 84 | This is an insane number, considering that for over 95% of the time cars are parked without doing anything at all. Robocars will eliminate the need for most parking spaces, and the few that remain can be used as temporary switch-locations. 85 | 86 | Banksy saw it right. 87 | This will free up to 27% of all land currently assigned for parking in cities, which we can finally turn into parks, making our cities more livable, beautiful, and healthy. 88 | Thanks to robocars, we can turn these: 89 | 90 | Into these: 91 | 92 | 93 | Let’s make our cities great again. 94 | Why we’ll see faster-than-ever adoption 95 | I’m an angel investor. I love startups, because they can solve big problems affecting hundreds of millions of people in just a few years. I invest in startups that have a great team, and can impact the greatest number of people positively in a short amount of time. Normal companies can’t do that. 96 | The secret ingredient is growth. A startup is a company that can grow and scale to a very large audience very quickly, and can do so consistently. 97 | Growth is fueled primarily by how big and how significant your key market differentiator is. The bigger the problem and the better the solution, the faster the growth. Startups are complicated beasts, but that’s really the essence. In short, we say: 98 | How much better is your product relative to the competition? 99 | If you provide a 10% improvement, you’ll probably never grow fast. Simple barriers to entry may be enough to discourage people to switch and begin using your product. 100 | If you offer a 90% improvement over the old technology, now we’re talking. That’s a 10x, which is often enough to have a clear key market differentiator and create entire new, billion-dollar markets. PayPal allowed users to send money across the world in minutes as opposed to days. That was easily a 100x improvement, and the company grew like crazy. 101 | Let’s have a look at how robocars play out in the startup equation: 102 | > 90% safer 103 | > 90% cheaper 104 | > 90% greener 105 | 90% faster 106 | 90% fewer cars 107 | 90% fewer parking spaces 108 | = > 1,000,000x improvement 109 | Each of these individual reasons would be enough to justify a switch to robocars, because each is 10x better relative to the old technology. Put together: 110 | Robocars represent at least million fold improvement. 111 | If robocars are 90% safer, it means we can expect only 10% of the accidents than the past. In reality, as our algorithms and sensors become exponentially more accurate and we gather more data, experts project a 99% reduction or more. In a few of decades, death by automobile accidents could become as rare as being struck by lightening. 112 | These are all conservative estimates. I could pick almost any of the reasons described above and make a case as to why each represents really a 99% improvement — or a factor of 100 — and not a “mere” 90%. 113 | A million fold improvement is in fact conservative. 114 | Very conservative. 115 | What we can learn from history 116 | Think of the last time something like this happened. 117 | 118 | Horse Drawn Carriages in New York city, 1917. Source. 119 | 100 years go we used horses to move around. Then the first commercial cars arrived. They didn’t go much faster than horses, but they were much easier to maintain (no manure to take care of), they could carry more people in a smaller space (an integrated system as opposite to having a separate carriage to attach), and could be left alone without having to constantly feed them (they primarily needed fuel, which could be put in when necessary). 120 | All in all, cars offered a 10–20x improvement over horses, yet the transition only took 20–30 years. Very soon, owning horses became a sport or a passion, not a means of transportation. 121 | 122 | Cars in the 1930s took over the cities. 123 | But perhaps the greatest advantage was that cars were an upgradable technology. If a new engine was invented, you could plug that in and get more power, ceteris paribus (leaving the rest unchanged). With horses, if you wanted more power, you had to purchase another horse and add it to the group, effectively doubling your spending, as well as your maintenance efforts and costs. 124 | Robocars can be streamlined, updated wirelessly, and they can drive themselves to the nearest maintenance center whenever needed. They offer a lot more advantages relative to cars than cars did relative to horses. 125 | A thought experiment 126 | Ask yourself this. You get a message from your friend to go to her place. She lives on the other side of town. Would you rather: 127 | 128 | Drive for more than an hour, be in agonizing traffic, spend $20 (all inclusive of insurance, gas, car usage), risk getting into an accident, only to spend another 10 minutes looking for parking and spending another $10? 129 | Or would you: 130 | 131 | Order a robocar, hop in, watch a YouTube video or take a nap in great comfort, have fun, be safe, and get out 10 minutes later, while spending $3 or less? 132 | 🚗 CAR 🤖 ROBOCAR 133 | Time spent 1h30m 10m 134 | Comfort level 2/10 9/10 135 | Safety level 1/10 9/10 136 | Money spent $30 $3 137 | 100 years ago, cars represented a mere 10x-20 improvement over horses, yet they conquered the market in 2–3 decades. 138 | Robocars represent a million fold improvement over cars. How long will it take for them to replace all vehicles on the road? 139 | What do I mean by robocars 140 | SAE defines the levels of driving automation as follows: 141 | 0 No Driving Automation 142 | 1 Driver Assistance 143 | 2 Partial Driving Automation 144 | 3 Conditional Driving Automation 145 | 4 High Driving Automation 146 | 5 Full Driving Automation 147 | I consider robocars to be level 5: full driving automation. 148 | Once car manufacturers and software companies fix the remaining technical issues (5 years give or take, Tesla is almost there already), regulation catches up (will taker a bit longer), and we adapt our infrastructure to accomodate them (a bit longer still), I predict most if not all new cars will be robocars. 149 | Once the first fully electric, fully autonomous robocar fleet enters the market, innovation will speed up, creating a virtuous cycle that will accelerate robocar adoption. At that point, there will be no point in building non-robocars, except for very niche markets (like sports). 150 | Here is another reason why it makes business sense. 151 | Robocars will double or triple the obtainable market 152 | The driver’s license is in decline, especially in the younger generation. Among teenagers, just 24.5 percent of 16-year-olds has a license, a 47-percent decrease from 1983, when 46.2 percent did. 153 | When asked why, people aged 18 to 39 said they were “too busy or not enough time to get a driver’s license” (37%), or that “owning and maintaining a vehicle is too expensive”(32%), or they were “able to get transportation from others” (31%).” 154 | Young people don’t like to get a driver’s license. We don’t like to own a car. We don’t want to maintain it. We want to move from point A to point B with the least amount of hassle and the most comfort. As public transportation became more ubiquitous, faster, and cheaper, we chose to use more of that. When Uber and other startups made it easier to take a taxi, we used more of those. 155 | When a new startup offers fully autonomous electric robocars, where the trips cost 10 times less, and are safer and more comfortable than regular old vehicles, the choice becomes pretty obvious. 156 | Companies that will fully embrace robocars as a service will quickly dominate the automotive market and create a trillion-dollar global industry. 157 | Robocars will allow not only adults without a license to move around, but also the elderly and people with disabilities. And why not, also children. 158 | The main reason parents don’t trust children to take public transportation is because they don’t feel it’s safe enough for them. While the vehicle itself may be relatively safe — at least as much as the car they drive — they don’t like the idea that their 6 year could be harassed by random strangers, or worse. 159 | With robocars, this concern becomes null. parents can call the robocar to their house and see their child get in. They can choose not to allow the car to pick anyone else up, and go directly to the school, football practice, or their friend’s house. They can track every movement on their smartphone, and even check how the kid’s doing from a security cam installed in the car, which will only turn on in certain circumstances, with the consent of the user, giving special access to parents with small children to monitor how they’re doing. 160 | Because the costs of running the car will drop exponentially, so might the margins for companies, one might think. But because we don’t need to pay attention to the street, we can do other things. 161 | I predict that in the future entertainment and third party services will become the most lucrative business in automotive. 162 | This will open up entire new markets, creating incentive for companies to join in the robocar ecosystem. 163 | Summary 164 | Robocars will make our street safer. With fewer accidents, they will save millions of lives every year from preventable deaths and injuries. 165 | They will make our cities greener. They will be fully electric, powered by renewable energies, and we’ll need less of them moving around. 166 | They will make us healthier. By reducing the need for parking, they will allow 27% of the space currently occupied in cities by parking and soulless concrete to become parks we can enjoy, taking walks and playing with friends, while breathing cleaner air. 167 | They will make transportation cheaper and more convenient. 168 | They will empower the disadvantaged, making personalized and ubiquitous transportation available to virtually everyone on Earth, including children, the elderly, the poor, and people with disabilities. 169 | They will save us time. Every year, billions of hours of cognitive potential is wasted in a mostly useless, tedious, and inefficient task. It will free our cognitive capacity to pursue greater things. 170 | Then again, if you really like driving and can’t do without it, you can always do it in VR while inside a robocar. 171 | What next? 172 | The advent of cars transformed our lives much more than people at the time could have anticipated. They transformed cities, gave birth to the suburbs, and engineered a social revolution. Their impact on society and the environment is so profound that it is difficult to comprehend what life was like before they came. We can get a glimpse from movies and books of the past. But really, we don’t know. 173 | So far, I only described the most obvious, and perhaps the most stupid ways robocars are going to change the way we live, because they are seen with the lenses of today. 174 | The real revolution will unfold in non-obvious ways, as emergent properties of a new complex system that we’ll put in place, which will create new possibilities and opportunities we probably can’t even think of now. 175 | 176 | For example, what happens when not only robocars become widespread, but when the entire transportation ecosystem around them also changes accordingly? 177 | Imagine this. You live in Rome. It’s a warm, sunny morning, you’re drinking a nice cappuccino at your house outside the city, when you get a call from a friend of yours living in Paris, he’s inviting you a party there. You request a robocar, and within 20 seconds it comes to pick you up. 178 | As you slide in and make yourself comfortable, the car already planned the entire journey, including the wireless charging routes to make sure it doesn’t need to stop at any point — unless you want to — along with the entertainment you like, a list of friends you might want to invite, and the work you wanted to catch up with before the end of the day. 179 | 180 | Concept station of Hyperloop Transportation Technologies. 181 | As you approach the main transportation hub, your robocar seamlessly enters inside a Hyperloop capsule, without reducing the current speed, sliding in one of the many evacuated tubes that connect to the larger hub. You accelerate to faster than the speed of sound, yet you don’t feel much different. You continue making videocalls, talking to friends, reading, and generally enjoying the trip. Less than an hour later you find yourself in front of your friend’s house in Paris, ready for the party. 182 | During this time, you didn’t have to deal with tolls or traffic. You didn’t have to get out of the car, charge it, speak to the police or show your passport. The robocar is equipped with with biometric identification, which you could also decide not to use it, if you want to. 183 | You remember back in the day, when going from one side of the city to the other — a mere 15 km — just to see your girlfriend, would sometimes take more than an hour, and how that over time took at toll on your relationship. 184 | Now, going to a city 1,500 km away seems like it’s around the corner. You can literally hop in, watch an episode of Game of Thrones, and you’re there. 185 | If this scenario seems too far fetched, it’s due a lack of imagination. As we speak, companies and startups are building each individual piece of technology to make this happen. The biggest obstacles remain laws and regulations, particularly the nonsensical security theatre around the so called “war on terror”. 186 | The future 187 | How will our lives change, once this becomes possible? Will we still decide to live in overcrowded cities, if we can get to any nearby point quickly, easily, hassle-free, and inexpensively? 188 | Perhaps we might see a new major shift in urbanization. As barriers to freely move are demolished, so will the need to squeeze in overcrowded, expensive and unhealthy city centers. 189 | Robocars might change the entire landscape of the world, how we build our homes and cities, how we live, and how we interact with one another. 190 | This is how a single technology can profoundly impact not just the automative industry, but the transportation sectors at large, the housing market, the job market, the entertainment industry, insurances, the communication sector, our laws regarding immigration, border control, our cities, our landscapes, and how it will empower entire segments of the population that currently don’t have the freedom to move. 191 | Just one technology.¹ 192 | Now think of everything else that’s coming. Genetic engineering. Ubiquitous sensors. Internet of things. Artificial Intelligence. Nanotechnology. 193 | These are not just buzzwords. They are not far-away science fiction. They are very real, they’re coming, and they’re going to change our lives in ways we can’t even comprehend. 194 | The future awaits. -------------------------------------------------------------------------------- /tests/driveless.txt-trans.html: -------------------------------------------------------------------------------- 1 | The Driverless Economy
Why robocars will be a bigger deal than you probably imagine

Concept art of a city of the future.
Since Google announced its first self-driving car prototypes back in 2012, people have been speculating how new technologies, AI, and automation may impact the global economy. Heck, some have even written entire books on this!
It’s hard not to spend some time wondering what’s going(n.去,离去,地面(或道路)的状况,工作情况,[复]行为adj.进行中的,流行的,现存的) to happen to the millions of truck and taxi drivers that are now roaming the streets, once this technological wonder becomes available to the masses. Will they all be out of a job? How are we to cope with such an event, especially at a time where governments are not exactly floating in cash — most are plagued by crippling national debt.
I have long suggested that we should explore and experiment with Universal Basic Income as a means to potentially restructure(vt.更改结构,重建构造,调整,改组) our society and overcome the looming problem of technological unemployment. I am not alone in thinking this. Over the years, Silicon Valley billionaires and internet entrepreneurs like Elon Musk, Sam Altman, and Mark Zuckerberg have taken a similar position.
When I first started giving lectures on this in 2012, audiences were split 90–10. The majority believed technological unemployment was a non-issue, which we could easily solve like we did with the past industrial revolutions.
Today, the ratio is still 90–10, but reversed. I have a hard time finding techno-skeptics.
However, while many have come around when it comes to the systematic loss of jobs, I find that:
Most people don’t have a full grasp of the profound implications that an automated economy is going(n.去,离去,地面(或道路)的状况,工作情况,[复]行为adj.进行中的,流行的,现存的) to bring. To explain it, perhaps there is no better and clearer example than autonomous vehicles.

One of the first prototypes of self-driving car developed by Google.
I will attempt to show how a single innovation is going(n.去,离去,地面(或道路)的状况,工作情况,[复]行为adj.进行中的,流行的,现存的) to completely disrupt not one, but several industries, with non-obvious rippling effects in almost all facets of our daily lives.
Note: for the sake of brevity, I’ll start referring to self-driving cars and the more general concept of autonomous vehicle — which includes cars, trucks, taxies, buses, vans, pods, trains, drones, and whatever comes next — as robocars. It’s shorter and gets the point across faster.
Also, I think it sounds cooler(n.冷却器).
90% safer
Robocars are filled with sensors. Different manufacturers use a combination of cameras, lasers, infrared(adj.红外线的n.红外线), RADAR, LiDAR, ultrasonic(adj.超音速的,超声的n.超声波), wheel speed sensors, passive visual, sonar(n.声纳,声波定位仪), GPS, accelerometer(n.加速计), gyroscope, temperature, humidity, and a bunch more.
Robocars can see and move around in rain, fog, and at night with no lights on. They can sense other robocars. They don’t need traffic lights. They can see 360° and be aware at all time of things we won’t ever be able to see.

Nearly 1.3 million people die in road crashes each year, an average of 3,287 deaths a day. An additional 20–50 million are injured or disabled.
Every. Single. Year.
Robocars can easily bring that number down 90%, and in the long run I predict over 99% — a factor of 100. The reduction of car accidents brought by the introduction of fully autonomous will be even more dramatic and sudden than that of airplanes.

In time, I predict that driving will become illegal, and be relegated to specific areas for driving enthusiasts. Just like hunting. A sport for the few.
Robocars won’t even be built with a steering wheel, because it will be an obsolete piece of design that won’t serve any purpose. They will be redesigned from scratch, allowing us to do a variety of things inside, and become more like an extension of our homes.
90% cheaper
The biggest costs of cars are gas, insurance, maintenance, and—when you take a taxi—the driver.
Robocars bring all of these costs down to practically zero.
With 90% fewer accidents, insurance costs will drop 90% or more.
Autonomous driving means you don’t need to pay for a human being behind the wheel, which is by far the biggest cost when one takes a taxi.
Robocars will be electric, and if you create your own electricity using solar PV or other methods, the cost of taking a trip will be practically zero. Electricity generation from solar is now cheaper than coal and gas in many countries, and this trend will continue in the future, bringing the cost down exponentially.

Credit: Ramez Naam.
Electric cars are still not mainstream due to mainly two reasons: range and cost. Both are improving exponentially, thanks to better battery technology and car design.
Robocars can leapfrog(n.跳背游戏(分开两腿从背弯站立者身上跳过),[军](两支部队)交互跃进,竞相提高vi.跳背,交替前进vt.跃过) and overcome these obstacles much faster than normal electric cars would. An organized fleet of robocars can be incredibly efficient at organizing routes and switches. You will not need to plan for long trips, look at the available charging stations along the way, or remember to charge your car during the night.
Robocars can do that automatically, so that you never have to think about it. Whether you have to do 10km or 10,000km, it doesn’t matter. It’s just something you will never be concerned with. A robocar will know which station to approach and at which time, it will come close to the appropriate replacement robocar whenever needed and open both doors for you. All you have to do is get out of one robocars and hop in the other every few hundred km. All in all, the process will take about 5 seconds, much less than would you’d need with any gasoline car and its refueling requirements.
Electric cars are also more reliable and require less maintenance. They are much simpler, have fewer parts, and can do most of their diagnostics via software, which can be regularly updated and controlled remotely.
Robocars don’t have to wait for electric vehicles to match the range of gasoline cars, or to even become as cheap.
The combined benefits of better logistics and reduced maintenance can make cost and range anxiety obsolete problem.
More flexible, more personalized
Robocars will also adjust to the needs of the individual person. It’s estimated that most cars move around with a single person inside, a huge waste of space and resources.
The average midsize(adj.中等大小的,中号的,中型的) car weighs 1,590 kilograms. The average human is 63kg. We essentially use a huge amount of energy to accelerate incredibly heavy objects, which take large amounts of space and weigh more than 20 times what they should be carrying.
That’s insane.
Aside from the failure to properly organize logistics with car pooling, the biggest issue is that we buy cars that are one-size fits all. We expect to be able to use them in cities, mountains, to bring kids to school, to go to work by ourselves, and to venture out for a weekend at the lake with friends. Also, we want to feel safe, so we buy increasingly bigger and more inefficient(adj.效率低的,效率差的,(指人)不能胜任的,无能的) cars.
This creates a vicious cycle.
With robocars, we don’t need to buy one that fits all sizes and uses. We can just order the robocar we need, when we need it, at a fraction of the cost.
For a trip alone, we could use single-occupant robocars, which are incredibly efficient and can zip around with ease anywhere in the city.

Concept car by Toyota for a single occupant robocar.
If we’re with a group of friends, we could share a small van.

And if we like to travel in luxury, we can do that as well.

In other words:
Robocars can be much cheaper, reliable, flexible, and comfortable than regulars cars, even if they have higher(adj.更高的) upfront costs and can’t drive as far.
They are better in every regard from day one.
A lot better.
90% faster
One of the main reason robocars are not already roaming the streets is the same reason we have traffic jams. Streets are filled with stupid, reckless humans at the wheel of coffin-shaped metal boxes, whizzing about inefficiently and dangerously.

We can’t even seem to manage to follow the simplest of rules, and we constantly cause unnecessary(adj.不必要的,多余的) traffic jams and accidents. If we simply kept the proper distance between cars, we could get rid of phantom traffic jams, which are infesting our highways. But humans are generally stupid when it comes to driving, and half of us are filled with testosterone(n.[生化][药] 睾丸激素), so we don’t.
A new study out of the University of Illinois at Urbana-Champaign suggests that the addition of just a small number of autonomous cars can ease the congestion on our roads. The presence of just one autonomous car reduces the standard deviation in speed of all the cars in the jam by around 50 percent, or a factor of 2.

In this replication(n.复制) of a phantom traffic jam, just a single car with limited autonomy (the silver SUV) is enough to clear up congestion involving 20 other cars. (source)
If all cars were autonomous, they could effectively communicate with each other, eliminate almost 100% of traffic jams, and always cruise at full speed, thus reducing commuting time by a factor of 10 at the very least.
90% fewer cars
Robocar rides will be cheap and ubiquitous. Unless you live in a remote, rural area, you will never need to buy a car. You might want to, but you won’t need to.
In cities and towns, fleets of robocars will be constantly moving around, ready to pick up anyone in need. And because they are connected and can talk to each other at the speed of light, they can organize logistics much better than we ever can or will.

A variety of advanced algorithms will optimize(vt.使最优化) the number of cars required, predict flows, incoming(adj.引入的) demand, be linked to weather stations and sensors on the roads. IoT, Big Data, and Deep Learning will play a huge role in this, feeding(n.给食,喂,饲养,吃,输送adj.给食的,饲用的,逐渐强烈的,供给饲料的,摄取食物的) each other in a virtuous cycle. More cheap and ubiquitous sensors around the cities means more and better quality data, which can train the algorithms to become more accurate.
In time, people will realize that owning a car in cities is not a good thing, but a drag. The younger generation already realized this, and it’s relying more on car sharing and public transport. Robocars offer all the pros, and none of the cons. They will blow every other means of transportation out of the water.
90% greener
This is a no brainer. With 90% fewer cars on the road, it follows that there will be a significant reduction in emissions as well.
However, the relationship might not be proportional. Because cars spend most of their time idle doing(n.做,干,行为,[复]活动,所作所为) nothing, they’re wasting valuable space, but they’re not emitting anything while sitting there. Robocars, on the other hand, would be constantly moving people around to optimize(vt.使最优化) use. This would increase emissions per car, but not per capita, it would roughly stay the same.
Now let’s factor in the other variables. Because of the dramatic decrease in traffic, congestions, and the optimized routes, we should expect an improvement of at least 4x, perhaps even 10x.
But we haven’t even scratched the surface.
The biggest reduction in emissions will come from the switch to electric vehicles.
Electric cars have virtually zero emissions, both of CO2 or harmful pollution, unless then one used to create them (which is more or less equivalent to that of regular cars).
The combined effect of better logistics, the elimination of traffic jams and the switch to electric will bring emissions down 10–1000x.
90% fewer parking spaces, 27% more free land area
It’s estimated that in the US there are 8 parking spots for every car, covering(n.遮盖物) up to 30% of our cities. Just let that sink in. Eight parking spaces for every single vehicle.

This is an insane number, considering that for over 95% of the time cars are parked without doing(n.做,干,行为,[复]活动,所作所为) anything at all. Robocars will eliminate the need for most parking spaces, and the few that remain can be used as temporary switch-locations.

Banksy saw(n.锯v.锯 vbl.see的过去式) it right.
This will free up to 27% of all land currently assigned for parking in cities, which we can finally turn into parks, making(n.制造,发展,素质) our cities more livable, beautiful, and healthy.
Thanks to robocars, we can turn these:

Into these:


Let’s make our cities great again.
Why we’ll see faster-than-ever adoption
I’m an angel investor. I love startups, because they can solve big problems affecting hundreds of millions of people in just a few years. I invest in startups that have a great team, and can impact the greatest number of people positively in a short amount of time. Normal companies can’t do that.
The secret ingredient is growth. A startup(n.[计]启动) is a company that can grow and scale to a very large audience very quickly, and can do so consistently.
Growth is fueled primarily by how big and how significant your key market differentiator(n.区分者,微分器) is. The bigger the problem and the better the solution, the faster the growth. Startups are complicated beasts, but that’s really the essence. In short, we say:
How much better is your product relative to the competition?
If you provide a 10% improvement, you’ll probably never grow fast. Simple barriers to entry may be enough to discourage people to switch and begin using your product.
If you offer a 90% improvement over the old technology, now we’re talking. That’s a 10x, which is often enough to have a clear key market differentiator(n.区分者,微分器) and create entire new, billion-dollar markets. PayPal allowed users to send money across the world in minutes as opposed to days. That was easily a 100x improvement, and the company grew like crazy.
Let’s have a look at how robocars play out in the startup(n.[计]启动) equation:
> 90% safer
> 90% cheaper
> 90% greener
90% faster
90% fewer cars
90% fewer parking spaces
= > 1,000,000x improvement
Each of these individual reasons would be enough to justify a switch to robocars, because each is 10x better relative to the old technology. Put together:
Robocars represent at least million fold improvement.
If robocars are 90% safer, it means we can expect only 10% of the accidents than the past. In reality, as our algorithms and sensors become exponentially more accurate and we gather more data, experts project a 99% reduction or more. In a few of decades, death by automobile accidents could become as rare as being struck by lightening.
These are all conservative estimates. I could pick almost any of the reasons described above and make a case as to why each represents really a 99% improvement — or a factor of 100 — and not a “mere” 90%.
A million fold improvement is in fact conservative.
Very conservative.
What we can learn from history
Think of the last time something like this happened.

Horse Drawn Carriages in New York city, 1917. Source.
100 years go we used horses to move around. Then the first commercial cars arrived. They didn’t go much faster than horses, but they were much easier to maintain (no manure to take care of), they could carry more people in a smaller space (an integrated system as opposite to having a separate carriage to attach), and could be left alone without having to constantly feed them (they primarily needed fuel, which could be put in when necessary).
All in all, cars offered a 10–20x improvement over horses, yet the transition only took 20–30 years. Very soon, owning horses became a sport or a passion, not a means of transportation.

Cars in the 1930s took over the cities.
But perhaps the greatest advantage was that cars were an upgradable(adj.能提到更高一级标准的(=upgradeable)) technology. If a new engine was invented, you could plug that in and get more power, ceteris paribus (leaving(n.离开) the rest unchanged(adj.无变化的,未改变的)). With horses, if you wanted more power, you had to purchase another horse and add it to the group, effectively doubling your spending, as well as your maintenance efforts and costs.
Robocars can be streamlined, updated wirelessly, and they can drive themselves to the nearest maintenance center whenever needed. They offer a lot more advantages relative to cars than cars did relative to horses.
A thought experiment
Ask yourself this. You get a message from your friend to go to her place. She lives on the other side of town. Would you rather:

Drive for more than an hour, be in agonizing traffic, spend $20 (all inclusive of insurance, gas, car usage), risk getting into an accident, only to spend another 10 minutes looking for parking and spending another $10?
Or would you:

Order a robocar, hop in, watch a YouTube video or take a nap in great comfort, have fun, be safe, and get out 10 minutes later, while spending $3 or less?
🚗 CAR 🤖 ROBOCAR
Time spent 1h30m 10m
Comfort level 2/10 9/10
Safety level 1/10 9/10
Money spent $30 $3
100 years ago, cars represented a mere 10x-20 improvement over horses, yet they conquered the market in 2–3 decades.
Robocars represent a million fold improvement over cars. How long will it take for them to replace all vehicles on the road?
What do I mean by robocars
SAE defines the levels of driving automation as follows:
0 No Driving Automation
1 Driver Assistance
2 Partial Driving Automation
3 Conditional Driving Automation
4 High Driving Automation
5 Full Driving Automation
I consider robocars to be level 5: full driving automation.
Once car manufacturers and software companies fix the remaining technical issues (5 years give or take, Tesla is almost there already), regulation catches up (will taker a bit longer), and we adapt our infrastructure to accomodate(v.容纳,留宿,使...适应) them (a bit longer still), I predict most if not all new cars will be robocars.
Once the first fully electric, fully autonomous robocar fleet enters the market, innovation will speed up, creating a virtuous cycle that will accelerate robocar adoption. At that point, there will be no point in building non-robocars, except for very niche markets (like sports).
Here is another reason why it makes business sense.
Robocars will double or triple the obtainable(adj.能得到的,可到手的) market
The driver’s license is in decline, especially in the younger generation. Among teenagers, just 24.5 percent of 16-year-olds has a license, a 47-percent decrease from 1983, when 46.2 percent did.
When asked why, people aged 18 to 39 said they were “too busy or not enough time to get a driver’s license” (37%), or that “owning and maintaining a vehicle is too expensive”(32%), or they were “able to get transportation from others” (31%).”
Young people don’t like to get a driver’s license. We don’t like to own a car. We don’t want to maintain it. We want to move from point A to point B with the least amount of hassle and the most comfort. As public transportation became more ubiquitous, faster, and cheaper, we chose to use more of that. When Uber and other startups made it easier to take a taxi, we used more of those.
When a new startup(n.[计]启动) offers fully autonomous electric robocars, where the trips cost 10 times less, and are safer and more comfortable than regular old vehicles, the choice becomes pretty obvious.
Companies that will fully embrace robocars as a service will quickly dominate the automotive(adj.汽车的,自动推进的) market and create a trillion-dollar global industry.
Robocars will allow not only adults without a license to move around, but also the elderly and people with disabilities. And why not, also children.
The main reason parents don’t trust children to take public transportation is because they don’t feel it’s safe enough for them. While the vehicle itself may be relatively safe — at least as much as the car they drive — they don’t like the idea that their 6 year could be harassed by random strangers, or worse.
With robocars, this concern becomes null. parents can call the robocar to their house and see their child get in. They can choose not to allow the car to pick anyone else up, and go directly to the school, football practice, or their friend’s house. They can track every movement on their smartphone, and even check how the kid’s doing(n.做,干,行为,[复]活动,所作所为) from a security cam(n.凸轮;Computer Aided Manufacturing,计算机辅助生产) installed in the car, which will only turn on in certain circumstances, with the consent of the user, giving special access to parents with small children to monitor how they’re doing(n.做,干,行为,[复]活动,所作所为).
Because the costs of running the car will drop exponentially, so might the margins for companies, one might think. But because we don’t need to pay attention to the street, we can do other things.
I predict that in the future entertainment and third party services will become the most lucrative business in automotive(adj.汽车的,自动推进的).
This will open up entire new markets, creating incentive for companies to join in the robocar ecosystem.
Summary
Robocars will make our street safer. With fewer accidents, they will save millions of lives every year from preventable(adj.可阻止的,可预防的) deaths and injuries.
They will make our cities greener. They will be fully electric, powered by renewable energies, and we’ll need less of them moving around.
They will make us healthier. By reducing the need for parking, they will allow 27% of the space currently occupied in cities by parking and soulless concrete to become parks we can enjoy, taking walks and playing with friends, while breathing cleaner air.
They will make transportation cheaper and more convenient.
They will empower(v.授权与,使能够) the disadvantaged, making(n.制造,发展,素质) personalized and ubiquitous transportation available to virtually everyone on Earth, including children, the elderly, the poor, and people with disabilities.
They will save us time. Every year, billions of hours of cognitive potential is wasted in a mostly useless, tedious, and inefficient(adj.效率低的,效率差的,(指人)不能胜任的,无能的) task. It will free our cognitive capacity to pursue greater things.
Then again, if you really like driving and can’t do without it, you can always do it in VR while inside a robocar.
What next?
The advent of cars transformed our lives much more than people at the time could have anticipated. They transformed cities, gave birth to the suburbs, and engineered a social revolution. Their impact on society and the environment is so profound that it is difficult to comprehend what life was like before they came. We can get a glimpse from movies and books of the past. But really, we don’t know.
So far, I only described the most obvious, and perhaps the most stupid ways robocars are going(n.去,离去,地面(或道路)的状况,工作情况,[复]行为adj.进行中的,流行的,现存的) to change the way we live, because they are seen with the lenses of today.
The real revolution will unfold in non-obvious ways, as emergent(adj.紧急的,浮现的,突然出现的,自然发生的) properties of a new complex system that we’ll put in place, which will create new possibilities and opportunities we probably can’t even think of now.

For example, what happens when not only robocars become widespread, but when the entire transportation ecosystem around them also changes accordingly?
Imagine this. You live in Rome. It’s a warm, sunny morning, you’re drinking a nice cappuccino(n.热牛奶咖啡) at your house outside the city, when you get a call from a friend of yours living in Paris, he’s inviting you a party there. You request a robocar, and within 20 seconds it comes to pick you up.
As you slide in and make yourself comfortable, the car already planned the entire journey, including the wireless charging routes to make sure it doesn’t need to stop at any point — unless you want to — along with the entertainment you like, a list of friends you might want to invite, and the work you wanted to catch up with before the end of the day.

Concept station of Hyperloop Transportation Technologies.
As you approach the main transportation hub, your robocar seamlessly enters inside a Hyperloop capsule, without reducing the current speed, sliding in one of the many evacuated tubes that connect to the larger hub. You accelerate to faster than the speed of sound, yet you don’t feel much different. You continue making(n.制造,发展,素质) videocalls, talking to friends, reading, and generally enjoying the trip. Less than an hour later you find yourself in front of your friend’s house in Paris, ready for the party.
During this time, you didn’t have to deal with tolls or traffic. You didn’t have to get out of the car, charge it, speak to the police or show your passport. The robocar is equipped with with biometric identification, which you could also decide not to use it, if you want to.
You remember back in the day, when going(n.去,离去,地面(或道路)的状况,工作情况,[复]行为adj.进行中的,流行的,现存的) from one side of the city to the other — a mere 15 km — just to see your girlfriend, would sometimes take more than an hour, and how that over time took at toll on your relationship.
Now, going(n.去,离去,地面(或道路)的状况,工作情况,[复]行为adj.进行中的,流行的,现存的) to a city 1,500 km away seems like it’s around the corner. You can literally hop in, watch an episode of Game of Thrones, and you’re there.
If this scenario seems too far fetched, it’s due a lack of imagination. As we speak, companies and startups are building each individual piece of technology to make this happen. The biggest obstacles remain laws and regulations, particularly the nonsensical(adj.无意义的,荒谬的) security theatre around the so called “war on terror”.
The future
How will our lives change, once this becomes possible? Will we still decide to live in overcrowded cities, if we can get to any nearby point quickly, easily, hassle-free, and inexpensively?
Perhaps we might see a new major shift in urbanization. As barriers to freely move are demolished, so will the need to squeeze in overcrowded, expensive and unhealthy(adj.不健康的,对健康有害的) city centers.
Robocars might change the entire landscape of the world, how we build our homes and cities, how we live, and how we interact with one another.
This is how a single technology can profoundly impact not just the automative industry, but the transportation sectors at large, the housing market, the job market, the entertainment industry, insurances, the communication sector, our laws regarding immigration, border control, our cities, our landscapes, and how it will empower(v.授权与,使能够) entire segments of the population that currently don’t have the freedom to move.
Just one technology.¹
Now think of everything else that’s coming(n.来达adj.就要来的,将来的,大有前途的). Genetic engineering. Ubiquitous sensors. Internet of things. Artificial Intelligence. Nanotechnology.
These are not just buzzwords. They are not far-away science fiction. They are very real, they’re coming(n.来达adj.就要来的,将来的,大有前途的), and they’re going(n.去,离去,地面(或道路)的状况,工作情况,[复]行为adj.进行中的,流行的,现存的) to change our lives in ways we can’t even comprehend.
The future awaits. -------------------------------------------------------------------------------- /vocabulary/highschool_edited.txt: -------------------------------------------------------------------------------- 1 | a 2 | an 3 | abandon 4 | ability 5 | able 6 | abnormal 7 | aboard 8 | abolish 9 | abortion 10 | about 11 | above 12 | abroad 13 | abrupt 14 | absence 15 | absent 16 | absolute 17 | absorb 18 | abstract 19 | absurd 20 | abundant 21 | abuse 22 | academic 23 | academy 24 | accelerate 25 | accent 26 | accept 27 | access 28 | accessible 29 | accident 30 | accommodation 31 | accompany 32 | accomplish 33 | account 34 | accountant 35 | accumulate 36 | accuracy 37 | accurate 38 | accuse 39 | accustomed 40 | ache 41 | achieve 42 | achievement 43 | acid 44 | acknowledge 45 | acquaintance 46 | acquire 47 | acquisition 48 | acre 49 | across 50 | act 51 | action 52 | active 53 | activity 54 | actor 55 | actress 56 | actual 57 | acute 58 | ad 59 | advertisement 60 | adapt 61 | 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1216 | feast 1217 | feather 1218 | federal 1219 | fee 1220 | feed 1221 | feel 1222 | feeling 1223 | fellow 1224 | female 1225 | fence 1226 | ferry 1227 | festival 1228 | fetch 1229 | fever 1230 | few 1231 | fibre 1232 | fiber 1233 | fiction 1234 | field 1235 | fierce 1236 | fight 1237 | figure 1238 | file 1239 | fill 1240 | film 1241 | final 1242 | finance 1243 | find 1244 | fine 1245 | finger 1246 | fingernail 1247 | finish 1248 | fire 1249 | fireworks 1250 | firm 1251 | fish 1252 | fist 1253 | fit 1254 | fix 1255 | flag 1256 | flame 1257 | flash 1258 | flashlight 1259 | flat 1260 | flee 1261 | flesh 1262 | flexible 1263 | flight 1264 | float 1265 | flood 1266 | floor 1267 | flour 1268 | flow 1269 | flower 1270 | flu 1271 | fluency 1272 | fluent 1273 | fly 1274 | fly 1275 | focus 1276 | fog 1277 | foggy 1278 | fold 1279 | folk 1280 | follow 1281 | fond 1282 | food 1283 | fool 1284 | foolish 1285 | foot 1286 | football 1287 | for 1288 | forbid 1289 | force 1290 | forecast 1291 | 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1714 | large 1715 | last 1716 | late 1717 | latter 1718 | laugh 1719 | laughter 1720 | laundry 1721 | law 1722 | lawyer 1723 | lay 1724 | lazy 1725 | lead 1726 | leader 1727 | leaf 1728 | league 1729 | leak 1730 | learn 1731 | least 1732 | leather 1733 | leave 1734 | lecture 1735 | left 1736 | leg 1737 | legal 1738 | lemon 1739 | lemonade 1740 | lend 1741 | length 1742 | lesson 1743 | let 1744 | letter 1745 | level 1746 | liberation 1747 | liberty 1748 | librarian 1749 | library 1750 | license 1751 | lid 1752 | lie 1753 | life 1754 | lift 1755 | light 1756 | lightning 1757 | like 1758 | likely 1759 | limit 1760 | line 1761 | link 1762 | lion 1763 | lip 1764 | liquid 1765 | list 1766 | listen 1767 | literature 1768 | literary 1769 | liter 1770 | litre 1771 | little 1772 | live 1773 | lively 1774 | load 1775 | loaf 1776 | local 1777 | lock 1778 | lonely 1779 | long 1780 | look 1781 | loose 1782 | lorry 1783 | lose 1784 | loss 1785 | lot 1786 | loud 1787 | lounge 1788 | love 1789 | lovely 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1864 | merciful 1865 | mercy 1866 | merely 1867 | merry 1868 | mess 1869 | message 1870 | messy 1871 | metal 1872 | method 1873 | metre 1874 | meter 1875 | microscope 1876 | microwave 1877 | middle 1878 | midnight 1879 | might 1880 | mild 1881 | mile 1882 | milk 1883 | millimetre 1884 | millimeter 1885 | millionaire 1886 | mind 1887 | mine 1888 | mineral 1889 | minibus 1890 | minimum 1891 | minister 1892 | ministry 1893 | minority 1894 | minus 1895 | minute 1896 | mirror 1897 | miss 1898 | missile 1899 | mist 1900 | mistake 1901 | mistaken 1902 | misunderstand 1903 | mix 1904 | mixture 1905 | mobile 1906 | model 1907 | modem 1908 | modern 1909 | modest 1910 | mom 1911 | mum 1912 | moment 1913 | mommy 1914 | mummy 1915 | money 1916 | monitor 1917 | monkey 1918 | month 1919 | monument 1920 | moon 1921 | mop 1922 | moral 1923 | more 1924 | morning 1925 | Moslem 1926 | mosquito 1927 | mother 1928 | motherland 1929 | motivation 1930 | motor 1931 | motorcycle 1932 | motto 1933 | mountain 1934 | mountainous 1935 | mourn 1936 | mouse 1937 | moustache 1938 | mouth 1939 | move 1940 | movement 1941 | movie 1942 | Mr. 1943 | Mrs. 1944 | Ms. 1945 | much 1946 | mud 1947 | muddy 1948 | multiply 1949 | murder 1950 | museum 1951 | mushroom 1952 | music 1953 | musical 1954 | musician 1955 | must 1956 | mustard 1957 | mutton 1958 | my 1959 | myself 1960 | nail 1961 | name 1962 | narrow 1963 | nation 1964 | national 1965 | nationality 1966 | nationwide 1967 | native 1968 | natural 1969 | nature 1970 | navy 1971 | near 1972 | nearby 1973 | nearly 1974 | neat 1975 | necessary 1976 | neck 1977 | necklace 1978 | need 1979 | needle 1980 | negotiate 1981 | neighbour 1982 | neighbor 1983 | neighbourhood 1984 | neighborhood 1985 | neither 1986 | nephew 1987 | nervous 1988 | nest 1989 | net 1990 | network 1991 | never 1992 | new 1993 | news 1994 | newspaper 1995 | next 1996 | nice 1997 | niece 1998 | no 1999 | No. 2000 | noble 2001 | nobody 2002 | nod 2003 | noise 2004 | noisy 2005 | none 2006 | noodle 2007 | noon 2008 | nor 2009 | normal 2010 | north 2011 | northeast 2012 | northern 2013 | northwest 2014 | nose 2015 | not 2016 | note 2017 | notebook 2018 | nothing 2019 | notice 2020 | novel 2021 | novelist 2022 | now 2023 | nowadays 2024 | nowhere 2025 | nuclear 2026 | numb 2027 | number 2028 | nurse 2029 | nursery 2030 | nut 2031 | nutrition 2032 | obey 2033 | object 2034 | observe 2035 | obtain 2036 | obvious 2037 | occupation 2038 | occupy 2039 | occur 2040 | ocean 2041 | Oceania 2042 | o'clock 2043 | of 2044 | off 2045 | offence 2046 | offer 2047 | office 2048 | officer 2049 | official 2050 | offshore 2051 | often 2052 | oh 2053 | oil 2054 | oilfield 2055 | OK 2056 | old 2057 | Olympic 2058 | Olympics 2059 | on 2060 | once 2061 | one 2062 | oneself 2063 | onion 2064 | only 2065 | onto 2066 | open 2067 | opening 2068 | opera 2069 | operate 2070 | operation 2071 | operator 2072 | opinion 2073 | oppose 2074 | opposite 2075 | optimistic 2076 | optional 2077 | or 2078 | oral 2079 | orange 2080 | orbit 2081 | order 2082 | ordinary 2083 | organ 2084 | organise 2085 | organize 2086 | organisation 2087 | organization 2088 | origin 2089 | other 2090 | otherwise 2091 | ought 2092 | our 2093 | ours 2094 | ourselves 2095 | out 2096 | outcome 2097 | outdoors 2098 | outer 2099 | outgoing 2100 | outing 2101 | outline 2102 | output 2103 | outside 2104 | outspoken 2105 | outstanding 2106 | outward 2107 | outwards 2108 | oval 2109 | over 2110 | overcoat 2111 | overcome 2112 | overhead 2113 | overlook 2114 | overweight 2115 | owe 2116 | own 2117 | owner 2118 | ownership 2119 | ox 2120 | oxygen 2121 | pace 2122 | Pacific 2123 | pack 2124 | package 2125 | packet 2126 | paddle 2127 | page 2128 | pain 2129 | painful 2130 | paint 2131 | painter 2132 | painting 2133 | pair 2134 | palace 2135 | pale 2136 | pan 2137 | pancake 2138 | panda 2139 | panic 2140 | paper 2141 | paperwork 2142 | paragraph 2143 | parallel 2144 | parcel 2145 | pardon 2146 | parent 2147 | park 2148 | parking 2149 | parrot 2150 | part 2151 | participate 2152 | particular 2153 | partly 2154 | partner 2155 | part-time 2156 | party 2157 | pass 2158 | passage 2159 | passenger 2160 | passer-by 2161 | passive 2162 | passport 2163 | past 2164 | patent 2165 | path 2166 | patience 2167 | patient 2168 | pattern 2169 | pause 2170 | pavement 2171 | pay 2172 | PC 2173 | P.E. 2174 | pea 2175 | peace 2176 | peaceful 2177 | peach 2178 | pear 2179 | pedestrian 2180 | pen 2181 | pence 2182 | pencil 2183 | penny 2184 | pension 2185 | people 2186 | pepper 2187 | per 2188 | percent 2189 | percentage 2190 | perfect 2191 | perform 2192 | performance 2193 | performer 2194 | perfume 2195 | perhaps 2196 | period 2197 | permanent 2198 | permission 2199 | permit 2200 | person 2201 | personal 2202 | personally 2203 | personnel 2204 | persuade 2205 | pest 2206 | pet 2207 | petrol 2208 | phenomenon 2209 | photo 2210 | photograph 2211 | photographer 2212 | phrase 2213 | physical 2214 | physician 2215 | physicist 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2288 | position 2289 | positive 2290 | possess 2291 | possession 2292 | possibility 2293 | possible 2294 | post 2295 | postage 2296 | postcard 2297 | postcode 2298 | poster 2299 | postman 2300 | postpone 2301 | pot 2302 | potato 2303 | potential 2304 | pound 2305 | pour 2306 | powder 2307 | power 2308 | powerful 2309 | practical 2310 | practice 2311 | practise 2312 | practice 2313 | praise 2314 | pray 2315 | prayer 2316 | precious 2317 | precise 2318 | predict 2319 | prefer 2320 | preference 2321 | pregnant 2322 | prejudice 2323 | premier 2324 | preparation 2325 | prepare 2326 | prescription 2327 | present 2328 | presentation 2329 | preserve 2330 | president 2331 | press 2332 | pressure 2333 | pretend 2334 | pretty 2335 | prevent 2336 | preview 2337 | precious 2338 | price 2339 | pride 2340 | primary 2341 | primitive 2342 | principle 2343 | print 2344 | prison 2345 | prisoner 2346 | private 2347 | privilege 2348 | prize 2349 | probably 2350 | problem 2351 | procedure 2352 | process 2353 | produce 2354 | product 2355 | production 2356 | profession 2357 | professor 2358 | profit 2359 | programme 2360 | program 2361 | progress 2362 | prohibit 2363 | project 2364 | promise 2365 | promote 2366 | pronounce 2367 | pronunciation 2368 | proper 2369 | properly 2370 | protect 2371 | protection 2372 | proud 2373 | prove 2374 | provide 2375 | province 2376 | psychology 2377 | pub 2378 | public 2379 | publish 2380 | pull 2381 | pulse 2382 | pump 2383 | punctual 2384 | punctuation 2385 | punish 2386 | punishment 2387 | pupil 2388 | purchase 2389 | pure 2390 | purple 2391 | purpose 2392 | purse 2393 | push 2394 | put 2395 | puzzle 2396 | pyramid 2397 | quake 2398 | qualification 2399 | quality 2400 | quantity 2401 | quarrel 2402 | quarter 2403 | queen 2404 | question 2405 | questionnaire 2406 | queue 2407 | quick 2408 | quiet 2409 | quilt 2410 | quit 2411 | quite 2412 | quiz 2413 | rabbit 2414 | race 2415 | racial 2416 | radiation 2417 | radio 2418 | radioactive 2419 | radium 2420 | rag 2421 | rail 2422 | railway 2423 | rain 2424 | rainbow 2425 | raincoat 2426 | rainfall 2427 | rainy 2428 | raise 2429 | random 2430 | range 2431 | rank 2432 | rapid 2433 | rare 2434 | rat 2435 | rate 2436 | rather 2437 | raw 2438 | ray 2439 | razor 2440 | reach 2441 | react 2442 | read 2443 | reading 2444 | ready 2445 | real 2446 | realise 2447 | realize 2448 | reality 2449 | really 2450 | reason 2451 | reasonable 2452 | rebuild 2453 | receipt 2454 | receive 2455 | receiver 2456 | recent 2457 | reception 2458 | receptionist 2459 | recipe 2460 | recite 2461 | recognise 2462 | recognize 2463 | recommend 2464 | record 2465 | recorder 2466 | recover 2467 | recreation 2468 | rectangle 2469 | recycle 2470 | red 2471 | reduce 2472 | refer 2473 | referee 2474 | reference 2475 | reflect 2476 | reform 2477 | refresh 2478 | refrigerator 2479 | refuse 2480 | regard 2481 | regardless 2482 | register 2483 | regret 2484 | regular 2485 | regulation 2486 | reject 2487 | relate 2488 | relation 2489 | relationship 2490 | relative 2491 | relax 2492 | relay 2493 | relevant 2494 | reliable 2495 | relief 2496 | religion 2497 | religious 2498 | rely 2499 | remain 2500 | remark 2501 | remember 2502 | remind 2503 | remote 2504 | remove 2505 | rent 2506 | repair 2507 | repeat 2508 | replace 2509 | reply 2510 | report 2511 | reporter 2512 | represent 2513 | representative 2514 | republic 2515 | reputation 2516 | request 2517 | require 2518 | requirement 2519 | rescue 2520 | research 2521 | resemble 2522 | reservation 2523 | reserve 2524 | resign 2525 | resist 2526 | respect 2527 | respond 2528 | responsibility 2529 | rest 2530 | restaurant 2531 | restriction 2532 | result 2533 | retell 2534 | retire 2535 | return 2536 | review 2537 | revision 2538 | revolution 2539 | reward 2540 | rewind 2541 | rhyme 2542 | rice 2543 | rich 2544 | rid 2545 | riddle 2546 | ridiculous 2547 | ride 2548 | right 2549 | rigid 2550 | ring 2551 | ripe 2552 | ripen 2553 | rise 2554 | risk 2555 | river 2556 | road 2557 | roast 2558 | rob 2559 | robot 2560 | rock 2561 | rocket 2562 | role 2563 | roll 2564 | roof 2565 | room 2566 | rooster 2567 | root 2568 | rope 2569 | rose 2570 | rot 2571 | rough 2572 | round 2573 | roundabout 2574 | routine 2575 | row 2576 | royal 2577 | rubber 2578 | rubbish 2579 | rude 2580 | rugby 2581 | ruin 2582 | rule 2583 | ruler 2584 | run 2585 | rush 2586 | sacred 2587 | sacrifice 2588 | sad 2589 | sadness 2590 | safe 2591 | safely 2592 | sail 2593 | sailor 2594 | salad 2595 | salary 2596 | sale 2597 | salesgirl 2598 | salesman 2599 | saleswoman 2600 | salt 2601 | salty 2602 | salute 2603 | same 2604 | sand 2605 | sandwich 2606 | satellite 2607 | satisfaction 2608 | satisfy 2609 | saucer 2610 | sausage 2611 | save 2612 | say 2613 | saying 2614 | scan 2615 | scar 2616 | scare 2617 | scarf 2618 | scene 2619 | scenery 2620 | sceptical 2621 | skeptical 2622 | schedule 2623 | scholar 2624 | scholarship 2625 | school 2626 | schoolbag 2627 | schoolboy 2628 | schoolgirl 2629 | schoolmate 2630 | science 2631 | scientific 2632 | scientist 2633 | scissors 2634 | scold 2635 | score 2636 | scratch 2637 | scream 2638 | screen 2639 | sculpture 2640 | sea 2641 | seagull 2642 | seal 2643 | search 2644 | seashell 2645 | seaside 2646 | season 2647 | seat 2648 | seaweed 2649 | second 2650 | secret 2651 | secretary 2652 | section 2653 | secure 2654 | security 2655 | see 2656 | seed 2657 | seek 2658 | seem 2659 | seize 2660 | seldom 2661 | select 2662 | selfish 2663 | sell 2664 | semicircle 2665 | seminar 2666 | send 2667 | senior 2668 | sense 2669 | sensitive 2670 | sentence 2671 | separate 2672 | separation 2673 | serious 2674 | servant 2675 | serve 2676 | service 2677 | session 2678 | set 2679 | settle 2680 | settlement 2681 | settler 2682 | several 2683 | sew 2684 | sex 2685 | shabby 2686 | shade 2687 | shadow 2688 | shake 2689 | shall 2690 | shallow 2691 | shame 2692 | shape 2693 | share 2694 | shark 2695 | sharp 2696 | sharpen 2697 | sharpener 2698 | shave 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terminal 3052 | terrible 3053 | terrify 3054 | terror 3055 | test 3056 | text 3057 | textbook 3058 | than 3059 | thank 3060 | thankful 3061 | that 3062 | the 3063 | theatre 3064 | theater 3065 | theft 3066 | their 3067 | theirs 3068 | them 3069 | theme 3070 | themselves 3071 | then 3072 | theoretical 3073 | theory 3074 | there 3075 | therefore 3076 | thermos 3077 | these 3078 | they 3079 | thick 3080 | thief 3081 | thin 3082 | thing 3083 | think 3084 | thinking 3085 | thirst 3086 | this 3087 | thorough 3088 | those 3089 | though 3090 | thought 3091 | thread 3092 | thrill 3093 | thriller 3094 | throat 3095 | through 3096 | throughout 3097 | throw 3098 | thunder 3099 | thunderstorm 3100 | thus 3101 | tick 3102 | ticket 3103 | tidy 3104 | tie 3105 | tiger 3106 | tight 3107 | till 3108 | time 3109 | timetable 3110 | tin 3111 | tiny 3112 | tip 3113 | tire 3114 | tired 3115 | tiresome 3116 | tissue 3117 | title 3118 | to 3119 | toast 3120 | tobacco 3121 | today 3122 | together 3123 | toilet 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3193 | try 3194 | T-shirt 3195 | tube 3196 | tune 3197 | turkey 3198 | turn 3199 | turning 3200 | tutor 3201 | TV 3202 | television 3203 | twice 3204 | twin 3205 | twist 3206 | type 3207 | typewriter 3208 | typical 3209 | typhoon 3210 | typist 3211 | tyre 3212 | tire 3213 | ugly 3214 | umbrella 3215 | unable 3216 | unbearable 3217 | unbelievable 3218 | uncertain 3219 | uncle 3220 | uncomfortable 3221 | unconditional 3222 | unconscious 3223 | under 3224 | underground 3225 | underline 3226 | understand 3227 | undertake 3228 | underwear 3229 | unemployment 3230 | unfair 3231 | unfit 3232 | unfortunate 3233 | uniform 3234 | union 3235 | unique 3236 | unit 3237 | unite 3238 | universal 3239 | universe 3240 | university 3241 | unless 3242 | unlike 3243 | unrest 3244 | until 3245 | unusual 3246 | unwilling 3247 | up 3248 | update 3249 | upon 3250 | upper 3251 | upset 3252 | upstairs 3253 | upward 3254 | upwards 3255 | urban 3256 | urge 3257 | urgent 3258 | use 3259 | used 3260 | useful 3261 | useless 3262 | user 3263 | usual 3264 | vacation 3265 | vacant 3266 | vague 3267 | vain 3268 | valid 3269 | valley 3270 | valuable 3271 | value 3272 | variety 3273 | various 3274 | vase 3275 | vast 3276 | VCD 3277 | vegetable 3278 | vehicle 3279 | version 3280 | vertical 3281 | very 3282 | vest 3283 | via 3284 | vice 3285 | victim 3286 | victory 3287 | video 3288 | videophone 3289 | view 3290 | village 3291 | villager 3292 | vinegar 3293 | violate 3294 | violence 3295 | violent 3296 | violin 3297 | violinist 3298 | virtue 3299 | virus 3300 | visa 3301 | visit 3302 | visitor 3303 | visual 3304 | vital 3305 | vivid 3306 | vocabulary 3307 | voice 3308 | volcano 3309 | volleyball 3310 | voluntary 3311 | volunteer 3312 | vote 3313 | voyage 3314 | wag 3315 | wage 3316 | waist 3317 | wait 3318 | waiter 3319 | waiting-room 3320 | waitress 3321 | wake 3322 | walk 3323 | wall 3324 | wallet 3325 | walnut 3326 | wander 3327 | want 3328 | war 3329 | ward 3330 | warehouse 3331 | warm 3332 | warmth 3333 | warn 3334 | wash 3335 | washroom 3336 | waste 3337 | watch 3338 | water 3339 | watermelon 3340 | wave 3341 | wax 3342 | way 3343 | we 3344 | web 3345 | website 3346 | weak 3347 | weakness 3348 | wealth 3349 | wealthy 3350 | wear 3351 | weather 3352 | wedding 3353 | weed 3354 | week 3355 | weekday 3356 | weekend 3357 | weekly 3358 | weep 3359 | weekend 3360 | weigh 3361 | weight 3362 | welcome 3363 | welfare 3364 | well 3365 | well 3366 | west 3367 | western 3368 | westward 3369 | westwards 3370 | wet 3371 | whale 3372 | what 3373 | whatever 3374 | wheat 3375 | wheel 3376 | when 3377 | whenever 3378 | where 3379 | wherever 3380 | whether 3381 | which 3382 | whichever 3383 | while 3384 | whisper 3385 | whistle 3386 | white 3387 | who 3388 | whole 3389 | whom 3390 | whose 3391 | why 3392 | wide 3393 | widespread 3394 | widow 3395 | wife 3396 | wild 3397 | wildlife 3398 | will 3399 | willing 3400 | win 3401 | wind 3402 | wind 3403 | window 3404 | windy 3405 | wine 3406 | wing 3407 | winner 3408 | winter 3409 | wipe 3410 | wire 3411 | wisdom 3412 | wise 3413 | wish 3414 | with 3415 | withdraw 3416 | within 3417 | without 3418 | witness 3419 | wolf 3420 | woman 3421 | wonder 3422 | wonderful 3423 | wood 3424 | wool 3425 | woollen 3426 | woolen 3427 | word 3428 | work 3429 | worker 3430 | world 3431 | worldwide 3432 | worm 3433 | worn 3434 | worried 3435 | worry 3436 | worth 3437 | worthwhile 3438 | worthy 3439 | would 3440 | wound 3441 | wrestle 3442 | wrinkle 3443 | wrist 3444 | write 3445 | wrong 3446 | X-ray 3447 | yard 3448 | yawn 3449 | year 3450 | yell 3451 | yellow 3452 | yes 3453 | yesterday 3454 | yet 3455 | yoghurt 3456 | you 3457 | young 3458 | your 3459 | yours 3460 | yourself 3461 | youth 3462 | yummy 3463 | zebra 3464 | zero 3465 | zip 3466 | zipper 3467 | zone 3468 | zoo 3469 | zoom -------------------------------------------------------------------------------- /vocabulary/WordList_TOEFL.txt: -------------------------------------------------------------------------------- 1 | abase 2 | abash 3 | abate 4 | abbreviate 5 | abet 6 | abhor 7 | abhorrent 8 | abide 9 | abiding 10 | ablaze 11 | abolish 12 | abominable 13 | abominate 14 | aborigines 15 | abridge 16 | abrupt 17 | absolve 18 | abstract 19 | abstruse 20 | abuse 21 | abyss 22 | accede 23 | accelerate 24 | accentuate 25 | access 26 | accessible 27 | accessory 28 | acclaim 29 | accommodate 30 | accompany 31 | accomplice 32 | accord 33 | accost 34 | account 35 | accredit 36 | accumulate 37 | accuse 38 | accustom 39 | acid 40 | acquaint 41 | acquiesce 42 | acquisitive 43 | acquit 44 | acrid 45 | acrimonious 46 | acrimony 47 | actuate 48 | acute 49 | adamant 50 | adapt 51 | adaptable 52 | addict 53 | adept 54 | adequate 55 | adhere 56 | adherent 57 | adhesive 58 | adjacent 59 | adjoin 60 | adjourn 61 | adjust 62 | administer 63 | admonish 64 | ado 65 | adolescence 66 | adopt 67 | adore 68 | adorn 69 | adroit 70 | adulate 71 | adulterate 72 | adverse 73 | adversity 74 | advocate 75 | aesthetic 76 | affable 77 | affectionate 78 | affiliate 79 | affinity 80 | affirm 81 | afflict 82 | affluent 83 | agenda 84 | aggrandize 85 | aggravate 86 | aggregate 87 | aghast 88 | agile 89 | agitate 90 | aglow 91 | agonize 92 | agrarian 93 | agreeable 94 | ail 95 | ailment 96 | air 97 | aisle 98 | akin 99 | alert 100 | alien 101 | alienate 102 | align 103 | allege 104 | allegiance 105 | allergic 106 | alleviate 107 | allocate 108 | allot 109 | alloy 110 | allude 111 | allure 112 | ally 113 | alms 114 | aloof 115 | alter 116 | alternate 117 | altitude 118 | amalgamate 119 | amass 120 | amateur 121 | ambiguous 122 | ambitious 123 | ambivalence 124 | ambivalent 125 | amble 126 | ambush 127 | ameliorate 128 | amenable 129 | amend 130 | amenity 131 | amiable 132 | amicable 133 | amiss 134 | amity 135 | ammunition 136 | amorphous 137 | ample 138 | amplify 139 | analogous 140 | anatomy 141 | ancestor 142 | anchor 143 | anecdote 144 | anguish 145 | animate 146 | annex 147 | annihilate 148 | anniversary 149 | annotate 150 | annoy 151 | annual 152 | annul 153 | anomalous 154 | anomaly 155 | anonymous 156 | antagonism 157 | antagonistic 158 | antecedent 159 | anthem 160 | antidote 161 | antique 162 | apathetic 163 | apathy 164 | aperture 165 | apex 166 | appall 167 | apparel 168 | appeal 169 | appease 170 | append 171 | appendage 172 | applaud 173 | appliance 174 | appraise 175 | apprehend 176 | apprehension 177 | apprehensive 178 | apprentice 179 | approach 180 | appropriate 181 | approximate 182 | apt 183 | aptitude 184 | aquatic 185 | arable 186 | arbitrary 187 | arbitrate 188 | arbitrator 189 | archives 190 | ardent 191 | arduous 192 | arid 193 | aristocrat 194 | armament 195 | armistice 196 | aroma 197 | aromatic 198 | array 199 | arrogant 200 | articulate 201 | artificial 202 | artillery 203 | ascend 204 | ascertain 205 | ascetic 206 | ascribe 207 | aspect 208 | aspire 209 | assail 210 | assassinate 211 | assault 212 | assay 213 | assemblage 214 | assemble 215 | assert 216 | assertion 217 | assess 218 | asset 219 | assiduous 220 | assimilate 221 | assorted 222 | assuage 223 | assumption 224 | astound 225 | astrology 226 | astronaut 227 | astute 228 | asylum 229 | atone 230 | atrocious 231 | attain 232 | attendant 233 | attenuate 234 | attest 235 | attic 236 | attire 237 | attorney 238 | attribute 239 | auction 240 | audacious 241 | audible 242 | auditor 243 | augment 244 | aura 245 | auspices 246 | auspicious 247 | austere 248 | austerity 249 | authentic 250 | authority 251 | authorize 252 | autocrat 253 | autonomous 254 | auxiliary 255 | avail 256 | available 257 | avarice 258 | avaricious 259 | avenge 260 | aver 261 | average 262 | averse 263 | aversion 264 | avert 265 | aviation 266 | avid 267 | avouch 268 | avow 269 | awe 270 | awry 271 | backbite 272 | backbone 273 | bacterium 274 | badge 275 | badger 276 | baffle 277 | bail 278 | bale 279 | balk 280 | ballot 281 | balmy 282 | ban 283 | banal 284 | bandage 285 | bandit 286 | banish 287 | bankrupt 288 | banter 289 | barbarous 290 | bargain 291 | barge 292 | bark 293 | barometer 294 | barren 295 | barrier 296 | barter 297 | base 298 | bashful 299 | bask 300 | baste 301 | bat 302 | bate 303 | batter 304 | bawl 305 | bear 306 | beckon 307 | becoming 308 | bedeck 309 | bedraggle 310 | begrudge 311 | behold 312 | belabor 313 | belated 314 | beleaguer 315 | belie 316 | belittle 317 | bellicose 318 | belligerent 319 | bellow 320 | benefactor 321 | benefit 322 | benevolence 323 | benevolent 324 | benign 325 | bent 326 | bequeath 327 | berate 328 | bereave 329 | beseech 330 | beset 331 | besmear 332 | bestow 333 | betoken 334 | bewilder 335 | bias 336 | bicker 337 | bigoted 338 | billow 339 | biting 340 | bizarre 341 | blame 342 | blanch 343 | bland 344 | blandish 345 | blank 346 | blast 347 | blatantly 348 | blaze 349 | bleach 350 | bleak 351 | blemish 352 | blench 353 | blend 354 | blessed 355 | blight 356 | blink 357 | bliss 358 | blithe 359 | blizzard 360 | bloat 361 | block 362 | blot 363 | blueprint 364 | bluff 365 | blunder 366 | blunt 367 | blur 368 | blush 369 | boast 370 | boastful 371 | boisterous 372 | bold 373 | bolster 374 | bombastic 375 | bond 376 | bonus 377 | boon 378 | boost 379 | booth 380 | booze 381 | bore 382 | bosom 383 | bossy 384 | bothersome 385 | bough 386 | bounce 387 | boundary 388 | boundless 389 | bounds 390 | bounteous 391 | bounty 392 | bouquet 393 | bout 394 | brace 395 | bracing 396 | brag 397 | braid 398 | branch 399 | brandish 400 | brawl 401 | brazen 402 | breach 403 | breathtaking 404 | breed 405 | breezy 406 | brevity 407 | brew 408 | bribe 409 | bridle 410 | brink 411 | brisk 412 | bristly 413 | brittle 414 | broach 415 | broil 416 | brood 417 | browbeat 418 | browse 419 | bruise 420 | brusque 421 | brutal 422 | bubble 423 | bud 424 | budget 425 | buff 426 | bulge 427 | bulk 428 | bulky 429 | bulletin 430 | bulwark 431 | bumpy 432 | bunch 433 | bundle 434 | bungle 435 | buoy 436 | buoyant 437 | burgeon 438 | burial 439 | burrow 440 | bustle 441 | cajole 442 | calamity 443 | caliber 444 | callous 445 | calumny 446 | camouflage 447 | campaign 448 | cancel 449 | candid 450 | candor 451 | canny 452 | canvas 453 | canyon 454 | capacious 455 | caper 456 | capitulate 457 | capricious 458 | capsize 459 | capsule 460 | caption 461 | captious 462 | captivate 463 | captive 464 | cardinal 465 | caress 466 | careworn 467 | cargo 468 | caricature 469 | carnal 470 | carp 471 | cascade 472 | cashier 473 | castigate 474 | cataract 475 | catching 476 | categorical 477 | categorize 478 | category 479 | cater 480 | cathedral 481 | caustic 482 | cavern 483 | cavity 484 | cede 485 | celebrity 486 | celestial 487 | censure 488 | centennial 489 | cerebral 490 | ceremonious 491 | certify 492 | chafe 493 | chamber 494 | channel 495 | chant 496 | chaos 497 | chapel 498 | charge 499 | charitable 500 | charity 501 | chase 502 | chasm 503 | chaste 504 | chastise 505 | check 506 | cherish 507 | chic 508 | chide 509 | chilly 510 | chirp 511 | chivalrous 512 | chivalry 513 | choke 514 | choosy 515 | chop 516 | chorus 517 | chronic 518 | chronicle 519 | chubby 520 | chunk 521 | circuit 522 | circuitous 523 | circular 524 | circulate 525 | circumscribe 526 | circumspect 527 | cite 528 | civility 529 | clamor 530 | clamorous 531 | clandestine 532 | clap 533 | clarify 534 | clasp 535 | classify 536 | clatter 537 | cleanse 538 | clench 539 | client 540 | climax 541 | cling 542 | clip 543 | cloak 544 | clog 545 | clot 546 | clue 547 | clumsy 548 | clutch 549 | clutter 550 | coagulate 551 | coarse 552 | coat 553 | coax 554 | code 555 | coerce 556 | coercion 557 | cogency 558 | cogent 559 | cogitate 560 | cohere 561 | coherence 562 | coherent 563 | coil 564 | coin 565 | coincide 566 | coincident 567 | coincidental 568 | collaborate 569 | collapse 570 | collate 571 | colleague 572 | collide 573 | collision 574 | colonize 575 | colossal 576 | coma 577 | combustion 578 | comical 579 | commemorate 580 | commence 581 | commend 582 | commendable 583 | commingle 584 | commiserate 585 | commit 586 | commodious 587 | commodity 588 | commonplace 589 | commute 590 | compact 591 | compassion 592 | compatible 593 | compel 594 | compensate 595 | competent 596 | compile 597 | complementary 598 | complex 599 | complexion 600 | compliant 601 | compliment 602 | complimentary 603 | comply 604 | component 605 | comport 606 | compose 607 | composed 608 | composure 609 | comprehensive 610 | compress 611 | comprise 612 | compromise 613 | compulsive 614 | compulsory 615 | conceal 616 | concede 617 | conceit 618 | conceited 619 | conceive 620 | concert 621 | concession 622 | conciliate 623 | conciliation 624 | conciliatory 625 | concise 626 | conclusive 627 | concoct 628 | concord 629 | concur 630 | concurrence 631 | concurrent 632 | condemn 633 | condense 634 | condescend 635 | condescending 636 | condole 637 | condone 638 | conducive 639 | confer 640 | confide 641 | confidential 642 | confine 643 | confirm 644 | confirmation 645 | confiscate 646 | conform 647 | confound 648 | confront 649 | congenial 650 | congenital 651 | congested 652 | conglomerate 653 | congregate 654 | congruous 655 | conjecture 656 | conjure 657 | connote 658 | conquest 659 | conscientious 660 | consecrate 661 | consecutive 662 | consent 663 | conservative 664 | conserve 665 | considerable 666 | considerate 667 | consign 668 | consistent 669 | console 670 | consort 671 | conspicuous 672 | conspiracy 673 | conspire 674 | constant 675 | constituent 676 | constitute 677 | constrain 678 | constraint 679 | construe 680 | consult 681 | consultative 682 | consume 683 | consummate 684 | contagion 685 | contagious 686 | contaminate 687 | contemplate 688 | contemptible 689 | contemptuous 690 | contend 691 | content 692 | contention 693 | contestant 694 | context 695 | contingent 696 | contort 697 | contract 698 | contradict 699 | contradictory 700 | contravene 701 | contribution 702 | contrite 703 | contrive 704 | controvert 705 | convalescent 706 | convene 707 | conventional 708 | convert 709 | convey 710 | convict 711 | conviction 712 | convoy 713 | convulse 714 | coordinate 715 | copious 716 | coral 717 | cordial 718 | corps 719 | corpulent 720 | corroborate 721 | corroborative 722 | corrode 723 | corrosive 724 | corrupt 725 | cosmopolitan 726 | costly 727 | costume 728 | counsel 729 | countenance 730 | counterfeit 731 | counterpart 732 | courteous 733 | courtesy 734 | covert 735 | covet 736 | covetous 737 | cower 738 | coy 739 | cozy 740 | cracker 741 | cradle 742 | craft 743 | crafty 744 | crag 745 | cram 746 | crash 747 | crave 748 | crease 749 | credentials 750 | credit 751 | creditable 752 | credulous 753 | creed 754 | creep 755 | crew 756 | crimson 757 | cripple 758 | crisp 759 | criterion 760 | critical 761 | crooked 762 | cross 763 | crucial 764 | crude 765 | cruise 766 | crumb 767 | crumble 768 | crust 769 | cryptic 770 | culmination 771 | culpable 772 | cult 773 | cumbersome 774 | curator 775 | curb 776 | currency 777 | current 778 | cursory 779 | curt 780 | curtail 781 | custodian 782 | cutting 783 | cyclone 784 | cynical 785 | dagger 786 | dainty 787 | damp 788 | dangle 789 | dank 790 | daring 791 | dart 792 | dash 793 | dated 794 | daunt 795 | dauntless 796 | daze 797 | dazzle 798 | deadly 799 | dearth 800 | debauch 801 | debris 802 | decadent 803 | decay 804 | deceit 805 | deceitful 806 | decency 807 | decent 808 | deception 809 | deceptive 810 | deciduous 811 | decipher 812 | decisive 813 | decline 814 | decorous 815 | decoy 816 | decree 817 | decrepit 818 | decry 819 | deduce 820 | deem 821 | defame 822 | defect 823 | defer 824 | deferential 825 | defiance 826 | defiant 827 | deficient 828 | deficit 829 | definite 830 | deflect 831 | deform 832 | defraud 833 | defray 834 | deft 835 | defunct 836 | defy 837 | degenerate 838 | degrade 839 | dehydrate 840 | deify 841 | deign 842 | dejected 843 | delegate 844 | delete 845 | deleterious 846 | deliberate 847 | delicate 848 | delineate 849 | delinquent 850 | delude 851 | deluge 852 | delusion 853 | demise 854 | demography 855 | demolish 856 | demonic 857 | demur 858 | demure 859 | denial 860 | denounce 861 | dense 862 | denunciation 863 | depict 864 | deplete 865 | deplorable 866 | deplore 867 | depose 868 | depot 869 | depreciate 870 | depress 871 | depression 872 | deprive 873 | deranged 874 | derelict 875 | deride 876 | derision 877 | derisive 878 | derive 879 | derogate 880 | derogatory 881 | descent 882 | deserted 883 | designate 884 | designing 885 | desolate 886 | despicable 887 | despoil 888 | despondent 889 | destined 890 | destitute 891 | detach 892 | detached 893 | detachment 894 | detain 895 | detect 896 | deter 897 | detergent 898 | deteriorate 899 | detest 900 | detract 901 | detrimental 902 | devastate 903 | deviate 904 | device 905 | devious 906 | devoid 907 | devotion 908 | devour 909 | devout 910 | dexterous 911 | diagram 912 | dialect 913 | dictator 914 | dictatorial 915 | diffident 916 | diffuse 917 | digest 918 | dignified 919 | dignify 920 | digress 921 | dilapidated 922 | dilate 923 | dilute 924 | dim 925 | dimension 926 | diminish 927 | diminution 928 | diminutive 929 | dingy 930 | diploma 931 | diplomacy 932 | diplomatic 933 | dire 934 | disarming 935 | disastrous 936 | disband 937 | discern 938 | discernible 939 | discharge 940 | discipline 941 | disclaim 942 | disclose 943 | discompose 944 | disconcert 945 | discord 946 | discount 947 | discourse 948 | discreet 949 | discrepancy 950 | discretion 951 | disdain 952 | disgraceful 953 | disgruntled 954 | disguise 955 | disintegration 956 | disinterested 957 | dismal 958 | dismantle 959 | dismay 960 | disparage 961 | dispel 962 | dispensable 963 | disperse 964 | display 965 | dispose 966 | disposition 967 | disproportionate 968 | disputatious 969 | disrupt 970 | dissect 971 | dissemble 972 | disseminate 973 | dissimulate 974 | dissipate 975 | dissolve 976 | distasteful 977 | distend 978 | distinction 979 | distinguish 980 | distort 981 | distract 982 | distraught 983 | distribute 984 | ditch 985 | diurnal 986 | dive 987 | diverge 988 | divergent 989 | diversify 990 | diversion 991 | divert 992 | divulge 993 | dizzy 994 | docile 995 | doctrine 996 | dodge 997 | dogma 998 | dogmatic 999 | dole 1000 | doleful 1001 | domain 1002 | domestic 1003 | domesticate 1004 | dominant 1005 | dominate 1006 | domineering 1007 | donate 1008 | dormant 1009 | dot 1010 | douse 1011 | dowdy 1012 | downcast 1013 | downhearted 1014 | doze 1015 | drab 1016 | draft 1017 | drain 1018 | drainage 1019 | drastic 1020 | drawback 1021 | drawl 1022 | dreadful 1023 | dreary 1024 | dregs 1025 | drench 1026 | dribble 1027 | drift 1028 | drizzle 1029 | drought 1030 | drowsy 1031 | drudge 1032 | dual 1033 | dubious 1034 | ductile 1035 | duel 1036 | dumbfounded 1037 | dumpy 1038 | duplicate 1039 | duplicity 1040 | durable 1041 | duration 1042 | dusk 1043 | dusky 1044 | dwarf 1045 | dwelling 1046 | dwindle 1047 | dynamic 1048 | earmark 1049 | earnest 1050 | eccentric 1051 | eclectic 1052 | eclipse 1053 | economical 1054 | economize 1055 | edible 1056 | edify 1057 | eerie 1058 | efface 1059 | efficacious 1060 | efficient 1061 | effusive 1062 | eject 1063 | elaborate 1064 | elapse 1065 | elastic 1066 | elate 1067 | elegant 1068 | elicit 1069 | eligible 1070 | eliminate 1071 | elite 1072 | elongate 1073 | eloquent 1074 | elucidate 1075 | elude 1076 | elusive 1077 | emaciated 1078 | emanate 1079 | emancipate 1080 | embark 1081 | embed 1082 | embellish 1083 | embezzle 1084 | emblem 1085 | embrace 1086 | embroil 1087 | embryo 1088 | emend 1089 | emergency 1090 | emigration 1091 | eminent 1092 | emit 1093 | emulate 1094 | enact 1095 | enchant 1096 | encircle 1097 | encompass 1098 | encounter 1099 | encroach 1100 | encumber 1101 | endeavor 1102 | endorse 1103 | endow 1104 | enervate 1105 | enforce 1106 | engender 1107 | engrave 1108 | engross 1109 | engulf 1110 | enhance 1111 | enigmatic 1112 | enlighten 1113 | enlist 1114 | enrage 1115 | enrapture 1116 | enrich 1117 | ensue 1118 | entail 1119 | entangle 1120 | entertain 1121 | enthral 1122 | entice 1123 | entrance 1124 | entrap 1125 | entreat 1126 | enumerate 1127 | enviable 1128 | envisage 1129 | epidemic 1130 | epigram 1131 | epilogue 1132 | epitome 1133 | equanimity 1134 | equitable 1135 | equivalence 1136 | equivalent 1137 | equivocal 1138 | equivocate 1139 | eradicate 1140 | erect 1141 | erode 1142 | errand 1143 | erratic 1144 | erroneous 1145 | erudite 1146 | erupt 1147 | escalate 1148 | eschew 1149 | escort 1150 | estate 1151 | esteemed 1152 | estrange 1153 | eternal 1154 | ethics 1155 | ethnic 1156 | eulogy 1157 | evacuate 1158 | evade 1159 | evaluate 1160 | evaporate 1161 | evasive 1162 | eventual 1163 | evict 1164 | evident 1165 | evoke 1166 | evolve 1167 | exacting 1168 | exaggerate 1169 | exalt 1170 | exalted 1171 | exasperate 1172 | excavate 1173 | exceed 1174 | excel 1175 | exceptional 1176 | excerpt 1177 | excessive 1178 | exclaim 1179 | exclude 1180 | exclusion 1181 | exclusive 1182 | execute 1183 | exemplary 1184 | exempt 1185 | exert 1186 | exhale 1187 | exhaust 1188 | exhaustive 1189 | exhilarate 1190 | exigencies 1191 | exile 1192 | exit 1193 | exonerate 1194 | exorbitant 1195 | exotic 1196 | expansive 1197 | expedient 1198 | expedite 1199 | expedition 1200 | expeditious 1201 | expel 1202 | expertise 1203 | expire 1204 | explicit 1205 | exposition 1206 | exposure 1207 | expound 1208 | expunge 1209 | exquisite 1210 | extant 1211 | extemporaneous 1212 | extemporize 1213 | extenuate 1214 | exterior 1215 | exterminate 1216 | external 1217 | extinct 1218 | extinguish 1219 | extol 1220 | extort 1221 | extract 1222 | extraneous 1223 | extravagant 1224 | extricate 1225 | exuberant 1226 | exult 1227 | exultant 1228 | fabled 1229 | fabricate 1230 | fabulous 1231 | facet 1232 | facetious 1233 | facile 1234 | facilitate 1235 | facility 1236 | faction 1237 | factitious 1238 | faculty 1239 | fade 1240 | faint 1241 | fallacious 1242 | fallacy 1243 | fallible 1244 | fallow 1245 | falter 1246 | fanatic 1247 | fanciful 1248 | fantastic 1249 | fantasy 1250 | far-fetched 1251 | far-reaching 1252 | farce 1253 | fascinate 1254 | fatal 1255 | fateful 1256 | fatigue 1257 | fatuous 1258 | fawn 1259 | feasible 1260 | feat 1261 | feeble 1262 | feign 1263 | felon 1264 | feminine 1265 | ferocious 1266 | fertile 1267 | fervent 1268 | fervor 1269 | fester 1270 | fete 1271 | fetter 1272 | feverish 1273 | fiasco 1274 | fickle 1275 | fictitious 1276 | fidelity 1277 | fidget 1278 | fiend 1279 | fiery 1280 | filch 1281 | file 1282 | filter 1283 | filth 1284 | filthy 1285 | fine 1286 | fiscal 1287 | fishy 1288 | fit 1289 | fitful 1290 | fitting 1291 | fixture 1292 | flaccid 1293 | flag 1294 | flagrant 1295 | flail 1296 | flair 1297 | flamboyant 1298 | flank 1299 | flare 1300 | flatter 1301 | flaunt 1302 | flaw 1303 | flay 1304 | fleck 1305 | flee 1306 | flick 1307 | flicker 1308 | flighty 1309 | flimsy 1310 | flinch 1311 | fling 1312 | flip 1313 | flippant 1314 | flirt 1315 | float 1316 | flock 1317 | flog 1318 | flop 1319 | florid 1320 | flounder 1321 | flourish 1322 | flout 1323 | fluctuate 1324 | fluffy 1325 | flush 1326 | fluster 1327 | flutter 1328 | foam 1329 | foe 1330 | foil 1331 | foist 1332 | foliage 1333 | folly 1334 | foment 1335 | fondle 1336 | foolhardy 1337 | foolproof 1338 | forage 1339 | forbid 1340 | forebear 1341 | forebode 1342 | foremost 1343 | forestall 1344 | forge 1345 | forlorn 1346 | formidable 1347 | formulate 1348 | forsake 1349 | forte 1350 | forthright 1351 | fortify 1352 | fortitude 1353 | fortuitous 1354 | foul 1355 | fowl 1356 | fraction 1357 | fractious 1358 | fracture 1359 | fragile 1360 | fragment 1361 | fragmentary 1362 | fragrance 1363 | fragrant 1364 | frail 1365 | frantic 1366 | fraternal 1367 | fraud 1368 | fraudulent 1369 | fraught 1370 | freight 1371 | frenzy 1372 | fret 1373 | friction 1374 | frigid 1375 | fringe 1376 | frivolous 1377 | frolic 1378 | frugal 1379 | frugality 1380 | frustrate 1381 | fulminate 1382 | fulsome 1383 | fumble 1384 | furious 1385 | furnish 1386 | furrow 1387 | furtive 1388 | fury 1389 | fuse 1390 | fuss 1391 | fussy 1392 | futile 1393 | fuzzy 1394 | gadget 1395 | gainsay 1396 | gait 1397 | gale 1398 | gallant 1399 | gallop 1400 | gangster 1401 | gape 1402 | garb 1403 | garbage 1404 | garnish 1405 | garrison 1406 | garrulous 1407 | gasp 1408 | gaudy 1409 | gauge 1410 | gay 1411 | gelatinous 1412 | gem 1413 | generate 1414 | generous 1415 | genial 1416 | genius 1417 | genteel 1418 | genuine 1419 | germane 1420 | germinate 1421 | gesture 1422 | ghastly 1423 | gibe 1424 | giddy 1425 | gigantic 1426 | gild 1427 | gilt 1428 | girdle 1429 | gist 1430 | glamor 1431 | glamorous 1432 | glance 1433 | glare 1434 | glaring 1435 | gleam 1436 | glean 1437 | gleeful 1438 | glib 1439 | gloat 1440 | gloom 1441 | gloomy 1442 | glossy 1443 | glue 1444 | glum 1445 | glut 1446 | gnaw 1447 | goad 1448 | gorge 1449 | gorgeous 1450 | gossip 1451 | gown 1452 | gracious 1453 | grand 1454 | grandiose 1455 | grapple 1456 | gratification 1457 | gratuitous 1458 | gravel 1459 | graze 1460 | grease 1461 | greasy 1462 | gregarious 1463 | grill 1464 | grim 1465 | grin 1466 | grind 1467 | grip 1468 | grisly 1469 | grit 1470 | groan 1471 | grope 1472 | gross 1473 | grotesque 1474 | grouchy 1475 | groundless 1476 | grove 1477 | grovel 1478 | growl 1479 | grudge 1480 | grudging 1481 | grueling 1482 | gruesome 1483 | gruff 1484 | grumble 1485 | grunt 1486 | guarantee 1487 | guffaw 1488 | guileless 1489 | guilt 1490 | gullible 1491 | gulp 1492 | gust 1493 | habitation 1494 | hackneyed 1495 | haggard 1496 | haggle 1497 | hail 1498 | halfhearted 1499 | hallucination 1500 | halt 1501 | hamlet 1502 | hamper 1503 | handicap 1504 | handy 1505 | haphazard 1506 | harass 1507 | hardheaded 1508 | hardhearted 1509 | hardy 1510 | harmony 1511 | harry 1512 | harsh 1513 | hasten 1514 | hatch 1515 | haughty 1516 | haul 1517 | haven 1518 | hazard 1519 | hazardous 1520 | hazy 1521 | head 1522 | headstrong 1523 | headway 1524 | heady 1525 | heal 1526 | hearsay 1527 | heave 1528 | hectic 1529 | heed 1530 | heedless 1531 | heinous 1532 | heir 1533 | herd 1534 | hereditary 1535 | heritage 1536 | hesitate 1537 | heterogeneous 1538 | hew 1539 | hide 1540 | hideous 1541 | highlight 1542 | hike 1543 | hilarious 1544 | hinder 1545 | hinge 1546 | hitch 1547 | hitchhike 1548 | hoard 1549 | hoarse 1550 | hoary 1551 | hoax 1552 | hobble 1553 | hoist 1554 | holocaust 1555 | homage 1556 | homely 1557 | homicide 1558 | homogeneous 1559 | hoodwink 1560 | hop 1561 | horrible 1562 | hospitable 1563 | hostage 1564 | hostile 1565 | hostility 1566 | hover 1567 | howl 1568 | hub 1569 | hubbub 1570 | huddle 1571 | hue 1572 | hug 1573 | humane 1574 | humbug 1575 | humid 1576 | humidity 1577 | humiliate 1578 | hunch 1579 | hurl 1580 | hush 1581 | husky 1582 | hustle 1583 | hygiene 1584 | hygienical 1585 | hypnotize 1586 | hypocrisy 1587 | hypocritical 1588 | hypothesis 1589 | iconoclast 1590 | identical 1591 | identify 1592 | ideology 1593 | idyllic 1594 | ignite 1595 | ignoble 1596 | ignominious 1597 | ignorant 1598 | ignore 1599 | illegible 1600 | illegitimate 1601 | illicit 1602 | illiterate 1603 | illuminate 1604 | illuminating 1605 | illusory 1606 | illustrate 1607 | illustrious 1608 | imbibe 1609 | imbue 1610 | immaculate 1611 | immerse 1612 | imminent 1613 | immortal 1614 | immune 1615 | impact 1616 | impair 1617 | impart 1618 | impartial 1619 | impasse 1620 | impassioned 1621 | impassive 1622 | impeccable 1623 | impede 1624 | impel 1625 | impending 1626 | imperative 1627 | imperious 1628 | impertinent 1629 | impervious 1630 | impetuous 1631 | impinge 1632 | implacable 1633 | implant 1634 | implement 1635 | implicate 1636 | implication 1637 | implicit 1638 | implore 1639 | importune 1640 | impose 1641 | imposing 1642 | impotence 1643 | imprecate 1644 | impromptu 1645 | improvident 1646 | improvise 1647 | imprudent 1648 | impudent 1649 | impugn 1650 | impulsive 1651 | impure 1652 | impute 1653 | inadvertent 1654 | inane 1655 | inanimate 1656 | inaugural 1657 | inaugurate 1658 | incapacitate 1659 | incarnate 1660 | incessant 1661 | incidence 1662 | incidental 1663 | incise 1664 | incisive 1665 | incite 1666 | incompatible 1667 | inconceivable 1668 | incorruptible 1669 | incredible 1670 | incubate 1671 | inculcate 1672 | incumbent 1673 | incur 1674 | indecent 1675 | indefatigable 1676 | indefinite 1677 | indelible 1678 | indent 1679 | index 1680 | indict 1681 | indifferent 1682 | indigenous 1683 | indignant 1684 | indolent 1685 | indomitable 1686 | induce 1687 | indulge 1688 | indulgent 1689 | industrious 1690 | ineligible 1691 | inept 1692 | inert 1693 | inertia 1694 | infamous 1695 | infatuated 1696 | infect 1697 | infectious 1698 | infest 1699 | infiltrate 1700 | infinite 1701 | infinitesimal 1702 | inflame 1703 | inflammable 1704 | inflate 1705 | inflexible 1706 | inflict 1707 | infringe 1708 | infuriate 1709 | infuse 1710 | ingenious 1711 | ingenuous 1712 | ingrained 1713 | ingredient 1714 | inhabitant 1715 | inherent 1716 | inhuman 1717 | initial 1718 | initiate 1719 | initiative 1720 | injurious 1721 | innate 1722 | innermost 1723 | innocent 1724 | innovator 1725 | innumerable 1726 | inordinate 1727 | inquiry 1728 | inquisitive 1729 | insane 1730 | inscribe 1731 | inscrutable 1732 | insert 1733 | insidious 1734 | insight 1735 | insinuate 1736 | insipid 1737 | insolent 1738 | inspire 1739 | instigate 1740 | instil 1741 | instinct 1742 | instinctive 1743 | institute 1744 | instruct 1745 | instrumental 1746 | insubordinate 1747 | insular 1748 | insurgent 1749 | insurrection 1750 | intact 1751 | intangible 1752 | integral 1753 | integrate 1754 | integrity 1755 | intense 1756 | intensify 1757 | intercede 1758 | intercept 1759 | interfere 1760 | interim 1761 | interior 1762 | interminable 1763 | intermittent 1764 | internal 1765 | intersect 1766 | interval 1767 | intervene 1768 | intimidate 1769 | intoxicate 1770 | intrepid 1771 | intricate 1772 | intrigue 1773 | introspection 1774 | intrude 1775 | inundate 1776 | inure 1777 | invalid 1778 | invalidate 1779 | invaluable 1780 | invariable 1781 | inverse 1782 | invert 1783 | inveterate 1784 | invocation 1785 | invoke 1786 | involuntary 1787 | irksome 1788 | irreconcilable 1789 | irrelevant 1790 | irrepressible 1791 | irreproachable 1792 | irresistible 1793 | irrevocable 1794 | irritable 1795 | iterate 1796 | itinerant 1797 | jargon 1798 | jaunt 1799 | jealous 1800 | jeer 1801 | jeopardize 1802 | jeopardy 1803 | jerk 1804 | jest 1805 | jettison 1806 | jog 1807 | jolly 1808 | jolt 1809 | jostle 1810 | jot 1811 | jovial 1812 | jubilant 1813 | judicious 1814 | juggle 1815 | jumble 1816 | juncture 1817 | jungle 1818 | junk 1819 | justification 1820 | justify 1821 | jut 1822 | juvenile 1823 | juxtapose 1824 | ken 1825 | kin 1826 | kindle 1827 | kit 1828 | knit 1829 | knuckle 1830 | laborious 1831 | laconic 1832 | lag 1833 | lament 1834 | lampoon 1835 | languid 1836 | languish 1837 | languor 1838 | lanky 1839 | lapse 1840 | lash 1841 | last 1842 | lasting 1843 | laud 1844 | laudable 1845 | launch 1846 | lavish 1847 | leak 1848 | leap 1849 | lease 1850 | lee 1851 | leer 1852 | legacy 1853 | legalize 1854 | legendary 1855 | legion 1856 | legislative 1857 | legitimate 1858 | lenient 1859 | lethal 1860 | levitate 1861 | levity 1862 | levy 1863 | liability 1864 | liable 1865 | liaison 1866 | liberal 1867 | licentious 1868 | lick 1869 | limb 1870 | limp 1871 | limpid 1872 | linger 1873 | lisp 1874 | listless 1875 | literacy 1876 | literal 1877 | lithe 1878 | litter 1879 | loan 1880 | loath 1881 | loathe 1882 | locate 1883 | locomotion 1884 | lodge 1885 | lodging 1886 | lofty 1887 | log 1888 | loiter 1889 | long 1890 | longevity 1891 | loop 1892 | loophole 1893 | loot 1894 | loquacious 1895 | lottery 1896 | lubricate 1897 | lucid 1898 | lucrative 1899 | ludicrous 1900 | lug 1901 | lukewarm 1902 | lull 1903 | lumber 1904 | luminous 1905 | lump 1906 | lunacy 1907 | lunatic 1908 | lunge 1909 | lurch 1910 | lure 1911 | lurid 1912 | lurk 1913 | luster 1914 | lustrous 1915 | lusty 1916 | luxuriant 1917 | luxurious 1918 | luxury 1919 | maculate 1920 | magnanimous 1921 | magnetic 1922 | magnify 1923 | maim 1924 | maintenance 1925 | malevolent 1926 | malice 1927 | malicious 1928 | malign 1929 | malignant 1930 | malleable 1931 | mandatory 1932 | maneuver 1933 | mangle 1934 | maniac 1935 | manifest 1936 | manifold 1937 | manipulate 1938 | mansion 1939 | manual 1940 | manure 1941 | mar 1942 | margin 1943 | marital 1944 | maritime 1945 | masculine 1946 | massacre 1947 | massive 1948 | maxim 1949 | maze 1950 | meager 1951 | mean 1952 | meander 1953 | meddle 1954 | mediate 1955 | mediocre 1956 | mediocrity 1957 | meditate 1958 | medley 1959 | meek 1960 | melancholy 1961 | melee 1962 | mellow 1963 | melody 1964 | menace 1965 | menial 1966 | merchandise 1967 | merciful 1968 | merge 1969 | merit 1970 | meritorious 1971 | mesmerize 1972 | mess 1973 | messy 1974 | mete 1975 | methodical 1976 | meticulous 1977 | metropolitan 1978 | mighty 1979 | migrate 1980 | mimic 1981 | mince 1982 | mingle 1983 | miniature 1984 | minimize 1985 | minimum 1986 | minute 1987 | miraculous 1988 | miscellaneous 1989 | mischief 1990 | mischievous 1991 | miserly 1992 | misgive 1993 | mishap 1994 | misrepresent 1995 | mist 1996 | mitigate 1997 | moan 1998 | mobile 1999 | mock 2000 | moderate 2001 | modest 2002 | modify 2003 | modulate 2004 | moist 2005 | mold 2006 | moldy 2007 | molest 2008 | mollify 2009 | molt 2010 | momentous 2011 | monarchy 2012 | monitor 2013 | monopolize 2014 | monopoly 2015 | monotonous 2016 | monotony 2017 | moody 2018 | morass 2019 | morbid 2020 | moribund 2021 | moron 2022 | morsel 2023 | mortal 2024 | mortgage 2025 | mortify 2026 | motto 2027 | mound 2028 | muddle 2029 | muffle 2030 | multitude 2031 | munch 2032 | mundane 2033 | munificent 2034 | murmur 2035 | muse 2036 | muster 2037 | musty 2038 | mutual 2039 | muzzle 2040 | mysterious 2041 | nadir 2042 | naivety 2043 | narcotic 2044 | narration 2045 | nasty 2046 | naught 2047 | nauseate 2048 | navigate 2049 | negate 2050 | negation 2051 | neglect 2052 | negligence 2053 | negligent 2054 | negotiate 2055 | negotiation 2056 | nepotism 2057 | nettle 2058 | neutrality 2059 | nibble 2060 | niche 2061 | nickname 2062 | nightmare 2063 | nimble 2064 | nocturnal 2065 | nominal 2066 | nominate 2067 | nonplus 2068 | norm 2069 | nostalgia 2070 | nostrum 2071 | notation 2072 | notion 2073 | notoriety 2074 | notorious 2075 | novelty 2076 | novice 2077 | noxious 2078 | nuance 2079 | nucleus 2080 | nudge 2081 | nuisance 2082 | null 2083 | nullify 2084 | numb 2085 | nurture 2086 | nutrition 2087 | oar 2088 | oasis 2089 | oath 2090 | obdurate 2091 | obedience 2092 | obese 2093 | objective 2094 | obligation 2095 | obligatory 2096 | obliging 2097 | oblique 2098 | obliterate 2099 | oblivion 2100 | oblivious 2101 | obnoxious 2102 | obscene 2103 | obscure 2104 | obsequious 2105 | observe 2106 | obsess 2107 | obsolescence 2108 | obsolete 2109 | obstacle 2110 | obstinate 2111 | obstruct 2112 | obtrude 2113 | obtrusive 2114 | obtuse 2115 | occult 2116 | odds 2117 | odious 2118 | odor 2119 | offence 2120 | offend 2121 | offensive 2122 | offhand 2123 | officious 2124 | offset 2125 | offspring 2126 | omen 2127 | ominous 2128 | omit 2129 | omnipotent 2130 | omniscient 2131 | onset 2132 | ooze 2133 | opponent 2134 | opportune 2135 | optical 2136 | optimal 2137 | optimism 2138 | option 2139 | opulence 2140 | opulent 2141 | oration 2142 | orator 2143 | orchestra 2144 | ordain 2145 | ordeal 2146 | orient 2147 | orientation 2148 | original 2149 | originality 2150 | originate 2151 | ornament 2152 | oscillate 2153 | ostensible 2154 | ostentation 2155 | ostracize 2156 | oust 2157 | outbreak 2158 | outburst 2159 | outcome 2160 | outfit 2161 | outlet 2162 | outline 2163 | outlook 2164 | outmoded 2165 | output 2166 | outrage 2167 | outrageous 2168 | outset 2169 | outskirts 2170 | outspoken 2171 | outstanding 2172 | ovation 2173 | overbearing 2174 | overdue 2175 | overflow 2176 | overlap 2177 | overlook 2178 | oversight 2179 | overstate 2180 | overt 2181 | overtake 2182 | overwhelm 2183 | overwhelming 2184 | pace 2185 | pacify 2186 | pact 2187 | painstaking 2188 | palatable 2189 | palliate 2190 | pallid 2191 | palpable 2192 | panacea 2193 | pang 2194 | panic 2195 | panorama 2196 | pant 2197 | parade 2198 | paradigm 2199 | paradox 2200 | paragon 2201 | parallel 2202 | paralyze 2203 | paramount 2204 | parasite 2205 | parch 2206 | parity 2207 | parody 2208 | paroxysm 2209 | parsimonious 2210 | parsimony 2211 | partake 2212 | partial 2213 | particle 2214 | partition 2215 | passionate 2216 | pasture 2217 | patch 2218 | patent 2219 | pathetic 2220 | patrol 2221 | patron 2222 | patronage 2223 | peck 2224 | peculate 2225 | peculiar 2226 | peddle 2227 | pedestrian 2228 | peek 2229 | peel 2230 | peer 2231 | peerless 2232 | pejorative 2233 | penalty 2234 | pending 2235 | penetrate 2236 | penetrating 2237 | penitence 2238 | pension 2239 | pensive 2240 | penury 2241 | perceive 2242 | perceptive 2243 | perch 2244 | perennial 2245 | perforate 2246 | perforation 2247 | perfume 2248 | perilous 2249 | perish 2250 | perjury 2251 | permanent 2252 | permeable 2253 | permeate 2254 | pernicious 2255 | perpendicular 2256 | perpetual 2257 | perplex 2258 | persecute 2259 | perseverance 2260 | persevere 2261 | persist 2262 | personable 2263 | perspective 2264 | perspire 2265 | pertinent 2266 | perturb 2267 | perverse 2268 | pervert 2269 | pester 2270 | petition 2271 | petrify 2272 | petulant 2273 | phase 2274 | phlegmatic 2275 | pickle 2276 | picturesque 2277 | piecemeal 2278 | pier 2279 | pierce 2280 | pilfer 2281 | pilgrim 2282 | pillage 2283 | pillar 2284 | pinch 2285 | pinnacle 2286 | pious 2287 | pit 2288 | pith 2289 | pittance 2290 | pivot 2291 | placate 2292 | placid 2293 | plague 2294 | plaintive 2295 | plank 2296 | plaster 2297 | plateau 2298 | plaudit 2299 | plausible 2300 | plea 2301 | plead 2302 | pledge 2303 | pliable 2304 | plight 2305 | plod 2306 | plot 2307 | plow 2308 | pluck 2309 | plumb 2310 | plume 2311 | plump 2312 | plunder 2313 | plunge 2314 | poise 2315 | polish 2316 | poll 2317 | pollute 2318 | pompous 2319 | ponder 2320 | ponderous 2321 | porcelain 2322 | portable 2323 | portend 2324 | portent 2325 | portray 2326 | portrayal 2327 | posterity 2328 | posthumous 2329 | posture 2330 | potent 2331 | potential 2332 | pouch 2333 | poultry 2334 | practice 2335 | prairie 2336 | prank 2337 | preach 2338 | precarious 2339 | precaution 2340 | precede 2341 | precept 2342 | precinct 2343 | precipitate 2344 | precipitation 2345 | precipitous 2346 | preclude 2347 | precocious 2348 | predecessor 2349 | predicament 2350 | predilection 2351 | predisposition 2352 | predominant 2353 | preeminent 2354 | pregnant 2355 | prejudice 2356 | preliminary 2357 | prelude 2358 | premature 2359 | premise 2360 | premium 2361 | premonition 2362 | preoccupation 2363 | preoccupy 2364 | preponderance 2365 | presage 2366 | prescience 2367 | prescription 2368 | present 2369 | presentation 2370 | preserve 2371 | preside 2372 | pressing 2373 | prestige 2374 | presumption 2375 | presumptuous 2376 | pretentious 2377 | pretext 2378 | prevail 2379 | prevalent 2380 | previous 2381 | prey 2382 | priceless 2383 | prick 2384 | prime 2385 | primitive 2386 | principal 2387 | privilege 2388 | probe 2389 | probity 2390 | procedure 2391 | proceed 2392 | proclaim 2393 | proclamation 2394 | procure 2395 | prod 2396 | prodigal 2397 | prodigious 2398 | profane 2399 | profess 2400 | profile 2401 | profound 2402 | profuse 2403 | progenitor 2404 | progeny 2405 | progression 2406 | prohibit 2407 | proliferate 2408 | prolific 2409 | prolong 2410 | prominent 2411 | promotion 2412 | prompt 2413 | promulgate 2414 | pronounced 2415 | prop 2416 | propaganda 2417 | propagate 2418 | propel 2419 | propensity 2420 | proportion 2421 | propound 2422 | propriety 2423 | prosaic 2424 | prosecute 2425 | prospect 2426 | prospective 2427 | prosper 2428 | prosperous 2429 | prototype 2430 | protract 2431 | protrude 2432 | provident 2433 | provision 2434 | provocation 2435 | provocative 2436 | provoke 2437 | prowess 2438 | proximity 2439 | prudence 2440 | prudent 2441 | prune 2442 | psyche 2443 | pulsate 2444 | pump 2445 | punch 2446 | punctual 2447 | punctuality 2448 | pungent 2449 | punitive 2450 | purchase 2451 | purge 2452 | purport 2453 | purse 2454 | pursue 2455 | puzzle 2456 | quack 2457 | quaint 2458 | qualification 2459 | qualify 2460 | qualm 2461 | quandary 2462 | quarantine 2463 | quarry 2464 | quash 2465 | queer 2466 | quell 2467 | quench 2468 | querulous 2469 | query 2470 | quest 2471 | quirk 2472 | quiver 2473 | quizzical 2474 | quorum 2475 | quota 2476 | quote 2477 | racket 2478 | radiance 2479 | radiant 2480 | radiate 2481 | radical 2482 | rage 2483 | ragged 2484 | raid 2485 | rally 2486 | ramble 2487 | rampant 2488 | random 2489 | range 2490 | rank 2491 | ransack 2492 | ransom 2493 | rap 2494 | rapacious 2495 | rapport 2496 | rapture 2497 | rash 2498 | ratify 2499 | ratio 2500 | ration 2501 | ravage 2502 | rave 2503 | ravenous 2504 | raze 2505 | realm 2506 | rear 2507 | rebate 2508 | rebel 2509 | rebellion 2510 | rebuff 2511 | rebuke 2512 | rebut 2513 | recalcitrant 2514 | recall 2515 | recapitulate 2516 | recede 2517 | receptacle 2518 | reception 2519 | recess 2520 | recession 2521 | recipe 2522 | recipient 2523 | reciprocal 2524 | reckless 2525 | reclaim 2526 | recluse 2527 | recoil 2528 | recollection 2529 | recompense 2530 | reconcile 2531 | reconnaissance 2532 | reconstruct 2533 | recreation 2534 | recruit 2535 | recur 2536 | recycle 2537 | redeem 2538 | redress 2539 | redundant 2540 | reef 2541 | referee 2542 | refine 2543 | refined 2544 | reflection 2545 | refrain 2546 | refresh 2547 | refuge 2548 | refund 2549 | refute 2550 | regime 2551 | regiment 2552 | regulate 2553 | regulation 2554 | rehabilitate 2555 | rehearse 2556 | reimburse 2557 | reinforce 2558 | reiterate 2559 | reject 2560 | rejoice 2561 | rejuvenate 2562 | relapse 2563 | release 2564 | relentless 2565 | relevance 2566 | relevant 2567 | reliance 2568 | relic 2569 | relief 2570 | relinquish 2571 | relish 2572 | remarkable 2573 | remedy 2574 | reminiscence 2575 | remiss 2576 | remnant 2577 | remorse 2578 | remorseful 2579 | remote 2580 | render 2581 | rendezvous 2582 | rendition 2583 | renounce 2584 | renovate 2585 | renovation 2586 | renown 2587 | renowned 2588 | repeal 2589 | repel 2590 | repellent 2591 | replenish 2592 | replica 2593 | replicate 2594 | repose 2595 | reprehend 2596 | reprisal 2597 | reprove 2598 | reptile 2599 | repudiate 2600 | repugnant 2601 | repulsive 2602 | repute 2603 | requisite 2604 | requisition 2605 | rescind 2606 | resentment 2607 | reservoir 2608 | reside 2609 | residual 2610 | residue 2611 | resign 2612 | resilient 2613 | resist 2614 | resolute 2615 | resolution 2616 | resolve 2617 | resonant 2618 | resort 2619 | resource 2620 | resourceful 2621 | respect 2622 | respiration 2623 | respite 2624 | restitution 2625 | restoration 2626 | restore 2627 | resume 2628 | retail 2629 | retain 2630 | retaliate 2631 | retard 2632 | reticence 2633 | reticent 2634 | retort 2635 | retract 2636 | retreat 2637 | retrieve 2638 | revelation 2639 | revenue 2640 | reverberate 2641 | reverence 2642 | reverent 2643 | reverse 2644 | revert 2645 | revive 2646 | revoke 2647 | reward 2648 | rhythm 2649 | riddle 2650 | ridicule 2651 | rift 2652 | rigid 2653 | rigor 2654 | rigorous 2655 | rim 2656 | rinse 2657 | riot 2658 | riotous 2659 | rip 2660 | ripe 2661 | ripple 2662 | rite 2663 | rival 2664 | roam 2665 | roar 2666 | robust 2667 | rote 2668 | rotten 2669 | rough 2670 | roundabout 2671 | rout 2672 | routine 2673 | rubble 2674 | ruby 2675 | ruddy 2676 | rudimentary 2677 | rueful 2678 | ruffian 2679 | ruffle 2680 | rugged 2681 | ruinous 2682 | rumble 2683 | ruminate 2684 | rummage 2685 | rumor 2686 | rupture 2687 | rural 2688 | rust 2689 | rustic 2690 | rusty 2691 | ruthless 2692 | sack 2693 | sacred 2694 | saddle 2695 | sag 2696 | saga 2697 | sagacious 2698 | saliva 2699 | salutary 2700 | salvage 2701 | salvation 2702 | salve 2703 | sample 2704 | sanction 2705 | sane 2706 | sanguine 2707 | sanitary 2708 | sanitation 2709 | sanity 2710 | sap 2711 | sarcasm 2712 | sardonic 2713 | satiate 2714 | satire 2715 | satirize 2716 | saturate 2717 | savor 2718 | savory 2719 | scale 2720 | scandal 2721 | scandalous 2722 | scanty 2723 | scar 2724 | scatter 2725 | scenic 2726 | scent 2727 | scheme 2728 | schism 2729 | scoff 2730 | scoop 2731 | scope 2732 | scorch 2733 | scorn 2734 | scornful 2735 | scourge 2736 | scramble 2737 | scrap 2738 | scrape 2739 | scratch 2740 | screech 2741 | scribble 2742 | script 2743 | scrub 2744 | scruple 2745 | scrupulous 2746 | scrutinize 2747 | scrutiny 2748 | scud 2749 | sculpture 2750 | scurry 2751 | seam 2752 | seasoning 2753 | seclude 2754 | seclusion 2755 | secrete 2756 | secretion 2757 | sect 2758 | secular 2759 | sedate 2760 | sedentary 2761 | sediment 2762 | seeming 2763 | seemly 2764 | seep 2765 | seethe 2766 | segment 2767 | segregate 2768 | seismic 2769 | self-esteem 2770 | semblance 2771 | sensation 2772 | sensational 2773 | sensible 2774 | sensuous 2775 | sentence 2776 | sentiment 2777 | sentry 2778 | sequence 2779 | serene 2780 | serenity 2781 | sermon 2782 | serpentine 2783 | servile 2784 | session 2785 | sever 2786 | severe 2787 | shackle 2788 | shady 2789 | shaft 2790 | shallow 2791 | sham 2792 | shear 2793 | sheathe 2794 | shed 2795 | shield 2796 | shift 2797 | shiftless 2798 | shifty 2799 | shiver 2800 | shove 2801 | shred 2802 | shrewd 2803 | shriek 2804 | shrink 2805 | shrivel 2806 | shroud 2807 | shrug 2808 | shun 2809 | shuttle 2810 | siege 2811 | sift 2812 | simulate 2813 | simultaneous 2814 | singe 2815 | sinister 2816 | sinuous 2817 | sip 2818 | site 2819 | skeleton 2820 | skeptic 2821 | skeptical 2822 | sketch 2823 | sketchy 2824 | skim 2825 | skimp 2826 | skimpy 2827 | skirmish 2828 | skulk 2829 | skull 2830 | skyrocket 2831 | slack 2832 | slacken 2833 | slag 2834 | slake 2835 | slander 2836 | slant 2837 | slap 2838 | slash 2839 | slaughter 2840 | sleek 2841 | slender 2842 | slick 2843 | slim 2844 | slippery 2845 | slither 2846 | slope 2847 | sloppy 2848 | slot 2849 | slough 2850 | slovenly 2851 | sluggish 2852 | slumber 2853 | slump 2854 | sly 2855 | smack 2856 | smash 2857 | smear 2858 | smother 2859 | smudge 2860 | smug 2861 | smuggle 2862 | snag 2863 | snare 2864 | snarl 2865 | snatch 2866 | sneak 2867 | sneer 2868 | snobbish 2869 | snout 2870 | snug 2871 | soak 2872 | soar 2873 | sober 2874 | sociable 2875 | soggy 2876 | sojourn 2877 | solace 2878 | sole 2879 | solemn 2880 | solicit 2881 | solicitous 2882 | solidarity 2883 | solitary 2884 | solitude 2885 | solvent 2886 | somber 2887 | sonorous 2888 | soothe 2889 | sophisticated 2890 | sordid 2891 | sore 2892 | sort 2893 | sound 2894 | sour 2895 | souvenir 2896 | sovereign 2897 | spacious 2898 | span 2899 | sparing 2900 | spark 2901 | sparse 2902 | spasm 2903 | spawn 2904 | species 2905 | specify 2906 | specimen 2907 | spectacle 2908 | spectacular 2909 | spectator 2910 | spectrum 2911 | speculate 2912 | speculative 2913 | spell 2914 | spew 2915 | sphere 2916 | spice 2917 | spill 2918 | spin 2919 | spineless 2920 | spiny 2921 | splendor 2922 | splice 2923 | splinter 2924 | split 2925 | spoil 2926 | spontaneous 2927 | sporadic 2928 | spot 2929 | spouse 2930 | spout 2931 | sprain 2932 | sprawl 2933 | spray 2934 | sprinkle 2935 | sprint 2936 | sprout 2937 | spur 2938 | spurt 2939 | squabble 2940 | squalid 2941 | squander 2942 | squash 2943 | squeak 2944 | squeamish 2945 | squeeze 2946 | stab 2947 | stack 2948 | stagger 2949 | stagnant 2950 | stain 2951 | stake 2952 | stale 2953 | stalk 2954 | stall 2955 | stamina 2956 | stand-in 2957 | staple 2958 | stark 2959 | startle 2960 | stately 2961 | static 2962 | stationary 2963 | stature 2964 | status 2965 | statute 2966 | staunch 2967 | steadfast 2968 | steer 2969 | sterile 2970 | stern 2971 | steward 2972 | stick 2973 | stigma 2974 | stimulate 2975 | stingy 2976 | stint 2977 | stipend 2978 | stipulate 2979 | stir 2980 | stock 2981 | stocky 2982 | stodgy 2983 | stoic 2984 | stoical 2985 | stolid 2986 | stoop 2987 | stout 2988 | straddle 2989 | strain 2990 | strait 2991 | strand 2992 | strap 2993 | strategy 2994 | stray 2995 | streak 2996 | strenuous 2997 | stress 2998 | stride 2999 | strident 3000 | strife 3001 | stringent 3002 | strip 3003 | stripe 3004 | strive 3005 | stroll 3006 | stub 3007 | studio 3008 | stuff 3009 | stuffy 3010 | stumble 3011 | stun 3012 | stunt 3013 | sturdy 3014 | suave 3015 | subdue 3016 | subjection 3017 | subjugate 3018 | sublimate 3019 | sublime 3020 | submissive 3021 | submit 3022 | subordinate 3023 | subscribe 3024 | subsequent 3025 | subservient 3026 | subsidiary 3027 | subsidy 3028 | subsist 3029 | subsistence 3030 | substantial 3031 | substantiate 3032 | substitute 3033 | subterranean 3034 | subtle 3035 | subvert 3036 | succeed 3037 | succession 3038 | succinct 3039 | succulent 3040 | succumb 3041 | suck 3042 | sue 3043 | sufficient 3044 | suffocate 3045 | suffrage 3046 | sulky 3047 | sullen 3048 | sultry 3049 | summarize 3050 | summary 3051 | summation 3052 | summit 3053 | summon 3054 | sumptuous 3055 | sundry 3056 | superb 3057 | superficial 3058 | superfluous 3059 | superintendent 3060 | supersede 3061 | superstition 3062 | supervise 3063 | supple 3064 | supplement 3065 | supremacy 3066 | supreme 3067 | surf 3068 | surfeit 3069 | surly 3070 | surmise 3071 | surmount 3072 | surpass 3073 | surplus 3074 | survey 3075 | surveyor 3076 | survive 3077 | susceptible 3078 | suspend 3079 | suspense 3080 | suspicion 3081 | suspicious 3082 | sustain 3083 | swamp 3084 | swarm 3085 | sway 3086 | swell 3087 | swelter 3088 | swerve 3089 | swindle 3090 | swing 3091 | swoop 3092 | symbol 3093 | symmetry 3094 | symposium 3095 | symptom 3096 | synchronize 3097 | synonymous 3098 | synopsis 3099 | synthesis 3100 | synthetic 3101 | syrup 3102 | tablet 3103 | taboo 3104 | tacit 3105 | taciturn 3106 | tack 3107 | tackle 3108 | tact 3109 | tactful 3110 | tactic 3111 | tag 3112 | taint 3113 | tale 3114 | talent 3115 | tally 3116 | tame 3117 | tamp 3118 | tamper 3119 | tangible 3120 | tangle 3121 | tantalize 3122 | tantamount 3123 | tap 3124 | tardy 3125 | target 3126 | tariff 3127 | tarnish 3128 | tart 3129 | taste 3130 | taunt 3131 | taut 3132 | tawny 3133 | tease 3134 | tedious 3135 | tedium 3136 | teem 3137 | telltale 3138 | temper 3139 | temperament 3140 | temperance 3141 | temperate 3142 | tempest 3143 | temporal 3144 | temporary 3145 | tempt 3146 | tenable 3147 | tenacious 3148 | tenant 3149 | tendency 3150 | tender 3151 | tenet 3152 | tenor 3153 | tense 3154 | tension 3155 | tentative 3156 | tenuous 3157 | tepid 3158 | term 3159 | terminate 3160 | terrific 3161 | terse 3162 | testify 3163 | testimony 3164 | testy 3165 | tether 3166 | textile 3167 | texture 3168 | thaw 3169 | therapeutic 3170 | thicket 3171 | thrash 3172 | threshold 3173 | thrift 3174 | thrifty 3175 | thrill 3176 | thrive 3177 | throb 3178 | throng 3179 | thrust 3180 | thump 3181 | thwart 3182 | tidings 3183 | tidy 3184 | tier 3185 | tile 3186 | tilt 3187 | timber 3188 | tinge 3189 | tint 3190 | tiny 3191 | tip 3192 | tirade 3193 | tissue 3194 | titanic 3195 | toil 3196 | token 3197 | tolerable 3198 | tolerate 3199 | toll 3200 | torment 3201 | tornado 3202 | torpid 3203 | torrid 3204 | tortuous 3205 | torture 3206 | torturous 3207 | toss 3208 | touching 3209 | touchy 3210 | tournament 3211 | tow 3212 | tract 3213 | tractable 3214 | traduce 3215 | trail 3216 | trait 3217 | traitor 3218 | tramp 3219 | trample 3220 | tranquil 3221 | transact 3222 | transcend 3223 | transcendental 3224 | transform 3225 | transient 3226 | transition 3227 | transitory 3228 | transmit 3229 | transparent 3230 | transpire 3231 | transpose 3232 | trash 3233 | traumatic 3234 | traverse 3235 | travesty 3236 | treacherous 3237 | treason 3238 | tremor 3239 | trench 3240 | trenchant 3241 | trend 3242 | trepidation 3243 | trial 3244 | tribute 3245 | trickle 3246 | trifle 3247 | trifling 3248 | trim 3249 | trivial 3250 | trophy 3251 | tropical 3252 | trot 3253 | truce 3254 | trudge 3255 | trying 3256 | tryst 3257 | tug 3258 | tumult 3259 | turbid 3260 | turbulent 3261 | turf 3262 | turmoil 3263 | twiddle 3264 | twig 3265 | twitch 3266 | typical 3267 | tyrant 3268 | tyro 3269 | ulterior 3270 | ultimate 3271 | ultimatum 3272 | unanimity 3273 | unanimous 3274 | unassuming 3275 | unattended 3276 | unbiased 3277 | unblemished 3278 | uncanny 3279 | uncouth 3280 | undergo 3281 | underline 3282 | undermine 3283 | undertake 3284 | undertaking 3285 | unearth 3286 | unfair 3287 | unfettered 3288 | uniform 3289 | unique 3290 | universal 3291 | unprecedented 3292 | unravel 3293 | unruly 3294 | unveil 3295 | unwitting 3296 | upbraid 3297 | upbringing 3298 | upgrade 3299 | upheaval 3300 | uphold 3301 | upright 3302 | uproar 3303 | uproarious 3304 | uproot 3305 | upset 3306 | urban 3307 | urbane 3308 | urge 3309 | urgent 3310 | usher 3311 | usurp 3312 | utensil 3313 | utilitarian 3314 | utility 3315 | utilize 3316 | vacancy 3317 | vacant 3318 | vacillate 3319 | vacillation 3320 | vacuum 3321 | vagary 3322 | vague 3323 | valiant 3324 | valid 3325 | valor 3326 | vandal 3327 | vanity 3328 | vapid 3329 | vapor 3330 | variant 3331 | variation 3332 | varnish 3333 | vary 3334 | veer 3335 | vehemence 3336 | vehement 3337 | vehicle 3338 | veil 3339 | vein 3340 | velocity 3341 | vend 3342 | vendor 3343 | venerable 3344 | venerate 3345 | vengeance 3346 | venom 3347 | venomous 3348 | vent 3349 | ventilation 3350 | veracity 3351 | verbose 3352 | verdant 3353 | verdict 3354 | verdure 3355 | verge 3356 | verify 3357 | vernal 3358 | versatile 3359 | versed 3360 | version 3361 | vertical 3362 | vessel 3363 | vestige 3364 | veteran 3365 | veto 3366 | vex 3367 | vibrant 3368 | vice 3369 | vicinity 3370 | vicious 3371 | victim 3372 | victor 3373 | vie 3374 | vigil 3375 | vigilant 3376 | vigor 3377 | vile 3378 | villain 3379 | vindicate 3380 | vintage 3381 | viper 3382 | virtual 3383 | virtue 3384 | virtuous 3385 | virulent 3386 | vision 3387 | visual 3388 | vital 3389 | vivacity 3390 | vocal 3391 | vocation 3392 | vociferous 3393 | vogue 3394 | void 3395 | volatile 3396 | volcano 3397 | volley 3398 | voluminous 3399 | voluntary 3400 | voracious 3401 | vouch 3402 | vouchsafe 3403 | vow 3404 | vulgar 3405 | vulnerable 3406 | waddle 3407 | wade 3408 | waft 3409 | wag 3410 | wager 3411 | wail 3412 | waive 3413 | wan 3414 | wane 3415 | wanish 3416 | wanton 3417 | ware 3418 | warehouse 3419 | warrant 3420 | warranty 3421 | wary 3422 | waterfall 3423 | wayward 3424 | weary 3425 | weird 3426 | wharf 3427 | whim 3428 | whimsical 3429 | whimsy 3430 | whirl 3431 | wholesale 3432 | wholesome 3433 | wield 3434 | wiggle 3435 | wiles 3436 | wily 3437 | wince 3438 | windy 3439 | wistful 3440 | wit 3441 | wither 3442 | withhold 3443 | withstand 3444 | witness 3445 | witty 3446 | wobble 3447 | woe 3448 | wont 3449 | worldly 3450 | worship 3451 | wrath 3452 | wreath 3453 | wreck 3454 | wrench 3455 | wrestle 3456 | wrinkle 3457 | writhe 3458 | wry 3459 | yacht 3460 | yarn 3461 | yearn 3462 | yell 3463 | yelp 3464 | yield 3465 | yoke 3466 | zeal 3467 | zealot 3468 | zealous 3469 | zenith 3470 | zone --------------------------------------------------------------------------------