├── trigrams.txt ├── davinci1.png ├── davinci2.jpg ├── davinci3.png ├── davinci4.png ├── davinci5.png ├── davinci6.jpg ├── LICENSE ├── .gitignore ├── davinci.py ├── README.md └── google-10000-english-usa.txt /trigrams.txt: -------------------------------------------------------------------------------- 1 | Please download your own trigrams file here: http://www.ngrams.info/download_coca.asp 2 | -------------------------------------------------------------------------------- /davinci1.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/codelucas/cracking-the-da-vinci-code-with-google-interview-problems-and-nlp-in-python/HEAD/davinci1.png -------------------------------------------------------------------------------- /davinci2.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/codelucas/cracking-the-da-vinci-code-with-google-interview-problems-and-nlp-in-python/HEAD/davinci2.jpg -------------------------------------------------------------------------------- /davinci3.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/codelucas/cracking-the-da-vinci-code-with-google-interview-problems-and-nlp-in-python/HEAD/davinci3.png -------------------------------------------------------------------------------- /davinci4.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/codelucas/cracking-the-da-vinci-code-with-google-interview-problems-and-nlp-in-python/HEAD/davinci4.png -------------------------------------------------------------------------------- /davinci5.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/codelucas/cracking-the-da-vinci-code-with-google-interview-problems-and-nlp-in-python/HEAD/davinci5.png -------------------------------------------------------------------------------- /davinci6.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/codelucas/cracking-the-da-vinci-code-with-google-interview-problems-and-nlp-in-python/HEAD/davinci6.jpg -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2017 Lucas Ou-Yang 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | # Byte-compiled / optimized / DLL files 2 | __pycache__/ 3 | *.py[cod] 4 | *$py.class 5 | 6 | # C extensions 7 | *.so 8 | 9 | # Distribution / packaging 10 | .Python 11 | env/ 12 | build/ 13 | develop-eggs/ 14 | dist/ 15 | downloads/ 16 | eggs/ 17 | .eggs/ 18 | lib/ 19 | lib64/ 20 | parts/ 21 | sdist/ 22 | var/ 23 | *.egg-info/ 24 | .installed.cfg 25 | *.egg 26 | 27 | # PyInstaller 28 | # Usually these files are written by a python script from a template 29 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 30 | *.manifest 31 | *.spec 32 | 33 | # Installer logs 34 | pip-log.txt 35 | pip-delete-this-directory.txt 36 | 37 | # Unit test / coverage reports 38 | htmlcov/ 39 | .tox/ 40 | .coverage 41 | .coverage.* 42 | .cache 43 | nosetests.xml 44 | coverage.xml 45 | *,cover 46 | .hypothesis/ 47 | 48 | # Translations 49 | *.mo 50 | *.pot 51 | 52 | # Django stuff: 53 | *.log 54 | local_settings.py 55 | 56 | # Flask stuff: 57 | instance/ 58 | .webassets-cache 59 | 60 | # Scrapy stuff: 61 | .scrapy 62 | 63 | # Sphinx documentation 64 | docs/_build/ 65 | 66 | # PyBuilder 67 | target/ 68 | 69 | # IPython Notebook 70 | .ipynb_checkpoints 71 | 72 | # pyenv 73 | .python-version 74 | 75 | # celery beat schedule file 76 | celerybeat-schedule 77 | 78 | # dotenv 79 | .env 80 | 81 | # virtualenv 82 | venv/ 83 | ENV/ 84 | 85 | # Spyder project settings 86 | .spyderproject 87 | 88 | # Rope project settings 89 | .ropeproject 90 | -------------------------------------------------------------------------------- /davinci.py: -------------------------------------------------------------------------------- 1 | import string 2 | import time 3 | 4 | 5 | TRIE_END = '__end' 6 | 7 | INPUT_COMMON_ENGLISH_WORDS = './google-10000-english-usa.txt' 8 | INPUT_TRIGRAMS = './trigrams.txt' 9 | 10 | 11 | def build_trie(): 12 | with open (INPUT_COMMON_ENGLISH_WORDS, 'r') as english_words: 13 | data = [line.strip() for line in english_words.readlines() 14 | if line.strip()] 15 | root = {} 16 | for word in data: 17 | current_dict = root 18 | for letter in word: 19 | current_dict = current_dict.setdefault(letter, {}) 20 | current_dict[TRIE_END] = True 21 | print('Trie contains %s words' % len(data)) 22 | return root 23 | 24 | 25 | def build_english_dict(): 26 | with open (INPUT_COMMON_ENGLISH_WORDS, 'r') as english_words: 27 | return set(line.strip() for line in english_words.readlines() 28 | if line.strip() and len(line.strip()) > 1) 29 | 30 | 31 | def find_bigrams(input_list): 32 | return zip(input_list, input_list[1:]) 33 | 34 | 35 | def is_trie_prefix(trie, word): 36 | if not word or not word.strip(): 37 | return False 38 | 39 | current_dict = trie 40 | for letter in word: 41 | if letter in current_dict: 42 | current_dict = current_dict[letter] 43 | else: 44 | return False 45 | return True 46 | 47 | 48 | TRIE = build_trie() 49 | ENGLISH_DICT = build_english_dict() 50 | 51 | skipped_recursion = 0 52 | 53 | 54 | def recurse(input_letters, current_permu, solution, final_len, word_end_index): 55 | global skipped_recursion 56 | # current permutation matches original anagram length, see if words 57 | # qualify as real sentence and dedupe, if valid, add to solution set 58 | if len(current_permu) == final_len: 59 | last_word = ''.join(current_permu[word_end_index:]) 60 | candidate_word = ''.join(current_permu) 61 | if last_word in ENGLISH_DICT and \ 62 | candidate_word not in solution: 63 | solution = solution.add(candidate_word) 64 | # print(candidate_word.upper()) 65 | return 66 | 67 | seen_at_start = set() # dedupe repeated characters in recursive depth 68 | for idx in range(len(input_letters)): 69 | cur = input_letters.pop(idx) 70 | if cur in seen_at_start: 71 | skipped_recursion += 1 72 | input_letters.insert(idx, cur) 73 | continue 74 | 75 | seen_at_start.add(cur) 76 | current_permu.append(cur) 77 | # spaces get special treatment since they distinguish words, 78 | # if a word is not valid english (via trie) we stop recursing 79 | if cur == ' ': 80 | # cut off extra space 81 | candidate_word = ''.join(current_permu[word_end_index:-1]) 82 | if candidate_word not in ENGLISH_DICT: # not is_trie_word(TRIE, candidate_word): 83 | # don't recurse - recent word chunk not valid 84 | skipped_recursion += 1 85 | pass 86 | else: 87 | recurse( 88 | input_letters, 89 | current_permu, 90 | solution, 91 | final_len, 92 | len(current_permu) 93 | ) 94 | else: 95 | if is_trie_prefix(TRIE, ''.join(current_permu[word_end_index:])): 96 | recurse( 97 | input_letters, 98 | current_permu, 99 | solution, 100 | final_len, 101 | word_end_index 102 | ) 103 | else: 104 | # don't recurse - word isn't valid, skip onto next one 105 | skipped_recursion += 1 106 | pass 107 | 108 | input_letters.insert(idx, cur) 109 | current_permu.pop() 110 | 111 | 112 | def transform(s): 113 | # remove punctution, make lowercase 114 | s = s.translate(None, string.punctuation) 115 | return s.lower() 116 | 117 | 118 | start = time.time() 119 | 120 | # orig_string = 'OH, LAME SAINT' 121 | orig_string = 'O, DRACONIAN DEVIL' 122 | xformed_string = transform(orig_string) 123 | xformed_string = list(xformed_string) 124 | 125 | # we sort the input characters so we can dedupe and skip recursive calls 126 | # orig = sorted(xformed_string) 127 | final_anagram_len = len(xformed_string) 128 | solution = set() 129 | 130 | recurse(xformed_string, [], solution, final_anagram_len, 0) 131 | print('After permuting the anagram %s has %s combinations' % 132 | (orig_string, len(solution))) 133 | 134 | end = time.time() 135 | print('Time elapsed is %s' % (end - start)) 136 | print('We skipped recursive calls %s times' % skipped_recursion) 137 | 138 | 139 | common_ngrams_dict = {} 140 | # now we need to build a map of the english language's most common bigrams, common bigrams 141 | # are referred to as collocations - two words that, together, are unusually common in 142 | # english texts. we use this technique to filter out the nonsense anagram combinations 143 | # to a sane number this file has just over 1M trigrams, about 17MB (that's fine) 144 | with open(INPUT_TRIGRAMS, 'r') as ngrams_data: 145 | clean_lines = [s.strip() for s in ngrams_data.read().splitlines()] 146 | for line in clean_lines: 147 | freq, w1, w2, w3 = line.split() 148 | common_ngrams_dict[(w1, w2, w3)] = int(freq) 149 | 150 | 151 | print('ngram dictionary contains %s words' % len(common_ngrams_dict)) 152 | 153 | filtered_solution = [] 154 | for anagram in solution: 155 | trigram = tuple(anagram.split()) 156 | if trigram in common_ngrams_dict: 157 | filtered_solution.append(anagram) 158 | 159 | 160 | print('After filtering anagrams without common trigrams, the anagram %s has %s combinations' % 161 | (orig_string, len(filtered_solution))) 162 | 163 | 164 | for anagram in filtered_solution: 165 | print(anagram.upper()) 166 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Cracking the Da Vinci Code with Google interview problems and NLP in python 2 | *Written by Lucas Ou-Yang* 3 | 4 | ![](./davinci1.png) 5 | 6 | I was rewatching The Da Vinci Code the other day and came across an incredible scene near the start where Robert and Sophie, the two leading protagonists playing detective roles, stumble across an anagram puzzle in the Louvre Museum in Paris. It was a dark night and their lives depended on them cracking the code quickly! Silas, the ruthless Opus Dei Zealot, was out for blood. 7 | 8 | Some vocabulary for those who aren't familiar: 9 | > An anagram is a word, phrase, or name formed by rearranging the letters of another word. For example: car => rac. These two words are anagrams of each other. 10 | 11 | In the Louvre that night, there was a dying man on the floor and beside him was a seemingly meaningless string of text. 12 | 13 | ![](./davinci2.jpg) 14 | 15 | O, DRACONIAN DEVIL! 16 | OH, LAME SAINT 17 | 18 | If we looked these two phrases up online, in any encyclopedia or reference manual, nothing meaningful will show up. Robert quickly arrived at the correct conclusion in the movie; that these two phrases were anagrams disguising two hidden and more meaningful phrases. (What likely gave it away is the difference in spelling of OH and O in the two phrases) 19 | 20 | For those of us plebs who don't have Ph.Ds in symbology, how can we systematically find out all the meaningful anagrams of a phrase like "O, DRACONIAN DEVIL!"? 21 | 22 | Thankfully, we have computers and algorithms to help! Let's tackle this problem step by step: 23 | 24 | Disclaimer: For the following algorithm we are assuming that: 25 | 26 | - The empty spaces count uniquely as characters in an anagram 27 | - Punctuation marks don't count 28 | - The anagrams are all in English 29 | 30 | *It wouldn't be hard to build an algorithm that doesn't need the above assumptions but I omitted that to simplify this post* 31 | 32 | A brute-force strategy would be to compute all of the possible permutations of "O DRACONIAN DEVIL" first, and then to examine each one. Because of this text's length of 17 characters, the number of permutations is 17! ~ seventeen factorial equals to about 355 trillion combinations! There is no way a human or small group of computers could even compute the possibilities -- nevertheless find out the correct one. We need something smarter. 33 | 34 | Computing all permutations is overkill. For example, a permutation of DEVIL is VLDEI, which is a meaningless word, it's not even valid english! Hence, we can change our permutation algorithm to immediately skip out of a computation once the current word in our phrase isn't a valid english word. We can also use this insight to skip out our computation if our current permutation isn't a prefix of a valid english word. 35 | 36 | How do we determine if a word is a valid English prefix or word? We can use a trie data structure holding the top 10,000 most commonly used English words to find prefixes. We can use a hashtable of the same 10,000 words to find valid words. We chose the 10K most common words because we want to keep the size of this trie smaller - A smaller trie means we have a tighter definition for "valid English word" which allows for a more efficient permutation algorithm. 37 | 38 | The big O-notation runtime is still O(n!) ~ 17! ~ 355 trillion because the upper-bound is that all permutations are not valid English words and also not valid prefixes. But, in practice / on average this algorithm is much more efficient since I got it to run in ~140 seconds on "O DRACONIAN DEVIL". We produced 1,458 valid permutations, a much more sane number than 355 trillion. With adding printlines we see that with the above heuristics we've skipped 65 million recursive calls! WOW! 39 | 40 | Here is an algorithm that enumerates all the sane permutations of "O, DRACONIAN DEVIL!". 41 | 42 | ```python 43 | import string 44 | import time 45 | 46 | TRIE_END = '__end' 47 | 48 | INPUT_COMMON_ENGLISH_WORDS = '/Users/lucasou-yang/google-10000-english-usa.txt' 49 | INPUT_TRIGRAMS = '/Users/lucasou-yang/trigrams.txt' 50 | 51 | 52 | def build_trie(): 53 | with open (INPUT_COMMON_ENGLISH_WORDS, 'r') as english_words: 54 | data = [line.strip() for line in english_words.readlines() 55 | if line.strip()] 56 | root = {} 57 | for word in data: 58 | current_dict = root 59 | for letter in word: 60 | current_dict = current_dict.setdefault(letter, {}) 61 | current_dict[TRIE_END] = True 62 | print('Trie contains %s words' % len(data)) 63 | return root 64 | 65 | 66 | def build_english_dict(): 67 | with open (INPUT_COMMON_ENGLISH_WORDS, 'r') as english_words: 68 | return set(line.strip() for line in english_words.readlines() 69 | if line.strip() and len(line.strip()) > 1) 70 | 71 | 72 | def find_bigrams(input_list): 73 | return zip(input_list, input_list[1:]) 74 | 75 | 76 | def is_trie_prefix(trie, word): 77 | if not word or not word.strip(): 78 | return False 79 | 80 | current_dict = trie 81 | for letter in word: 82 | if letter in current_dict: 83 | current_dict = current_dict[letter] 84 | else: 85 | return False 86 | return True 87 | 88 | 89 | TRIE = build_trie() 90 | ENGLISH_DICT = build_english_dict() 91 | 92 | skipped_recursion = 0 93 | 94 | 95 | def recurse(input_letters, current_permu, solution, final_len, word_end_index): 96 | global skipped_recursion 97 | # current permutation matches original anagram length, see if words 98 | # qualify as real sentence and dedupe, if valid, add to solution set 99 | if len(current_permu) == final_len: 100 | last_word = ''.join(current_permu[word_end_index:]) 101 | candidate_word = ''.join(current_permu) 102 | if last_word in ENGLISH_DICT and \ 103 | candidate_word not in solution: 104 | solution = solution.add(candidate_word) 105 | # print(candidate_word.upper()) 106 | return 107 | 108 | seen_at_start = set() # dedupe repeated characters in recursive depth 109 | for idx in range(len(input_letters)): 110 | cur = input_letters.pop(idx) 111 | if cur in seen_at_start: 112 | skipped_recursion += 1 113 | input_letters.insert(idx, cur) 114 | continue 115 | 116 | seen_at_start.add(cur) 117 | current_permu.append(cur) 118 | # spaces get special treatment since they distinguish words, 119 | # if a word is not valid english (via trie) we stop recursing 120 | if cur == ' ': 121 | # cut off extra space 122 | candidate_word = ''.join(current_permu[word_end_index:-1]) 123 | if candidate_word not in ENGLISH_DICT: # not is_trie_word(TRIE, candidate_word): 124 | # don't recurse - recent word chunk not valid 125 | skipped_recursion += 1 126 | pass 127 | else: 128 | recurse( 129 | input_letters, 130 | current_permu, 131 | solution, 132 | final_len, 133 | len(current_permu) 134 | ) 135 | else: 136 | if is_trie_prefix(TRIE, ''.join(current_permu[word_end_index:])): 137 | recurse( 138 | input_letters, 139 | current_permu, 140 | solution, 141 | final_len, 142 | word_end_index 143 | ) 144 | else: 145 | # don't recurse - word isn't valid, skip onto next one 146 | skipped_recursion += 1 147 | pass 148 | 149 | input_letters.insert(idx, cur) 150 | current_permu.pop() 151 | 152 | 153 | def transform(s): 154 | # remove punctution, make lowercase 155 | s = s.translate(None, string.punctuation) 156 | return s.lower() 157 | 158 | 159 | start = time.time() 160 | 161 | # orig_string = 'OH, LAME SAINT' 162 | orig_string = 'O, DRACONIAN DEVIL' 163 | xformed_string = transform(orig_string) 164 | xformed_string = list(xformed_string) 165 | 166 | # we sort the input characters so we can dedupe and skip recursive calls 167 | # orig = sorted(xformed_string) 168 | final_anagram_len = len(xformed_string) 169 | solution = set() 170 | 171 | recurse(xformed_string, [], solution, final_anagram_len, 0) 172 | print('After permuting the anagram %s has %s combinations' % 173 | (orig_string, len(solution))) 174 | 175 | end = time.time() 176 | print('Time elapsed is %s' % (end - start)) 177 | print('We skipped recursive calls %s times' % skipped_recursion) 178 | ``` 179 | 180 | Running the code returns 1,458 unique candidates for the decoded anagram of "O, DRACONIAN DEVIL!". 181 | 182 | ![](./davinci3.png) 183 | 184 | ![](./davinci4.png) 185 | 186 | If Robert and Sophie had all afternoon to examine the 1,458 phrases I guess that would be fine .. but we can do better! How can we now algorithmically extract all the most meaningful candidates from this result set of 1,458 anagrams? It would be nice to narrow the candidate set down to, say < 10 anagrams. 187 | 188 | We already know that the existing candidates in the 1,458 anagrams are all valid English words since we've done that filtering previously. So, given that we've applied syntactic filtering now would be a good time to apply semantic filtering. 189 | 190 | How about a strategy that filters out the candidates that have meaningless phrases. Can "VOID NONE RADICAL" or "VON RADIO ICELAND" possibly mean anything? The text isn't valid English once again. The of words "von", "radio", "iceland", "void", and "none" are all real English words but combined they are meaningless. 191 | 192 | Several solutions to semantic filtering come up: 193 | - Perform [NLP](https://en.wikipedia.org/wiki/Natural_language_processing) analysis on every phrase, remove all the phrases that aren't in the form of valid English grammar. 194 | - Perform NLP analysis but do it in the form of common [bigram or trigram](https://en.wikipedia.org/wiki/Bigram) filtering, keep the anagrams that are collocated trigrams. *Collocations are expressions of multiple words which commonly co-occur. For example: "New York" or "going to".* 195 | Use [IDF](https://nlp.stanford.edu/IR-book/html/htmledition/inverse-document-frequency-1.html) and keep anagrams which have documents with high IDF scores. (we can't use [TF-IDF](https://en.wikipedia.org/wiki/Tf%E2%80%93idf) since the TF in this case wouldn't make sense, a list of permutations isn't a document). 196 | 197 | There are likely other strategies but I ended up implementing #2. It worked amazingly. Here is the code (I loaded into a hashtable the 1 million most common trigrams found in the [Corpus of Contemporary American English](http://corpus.byu.edu/coca/) found on this [website](http://www.ngrams.info/download_coca.asp)). The trigram solution lends itself very well to this problem since our original anagram "O, DRACONIAN DEVIL!" has two spaces and three words - which makes it a trigram. We also don't necessarily need to use trigrams to solve this, bigrams also works. 198 | 199 | ```python 200 | # orig_string = 'OH, LAME SAINT' 201 | orig_string = 'O, DRACONIAN DEVIL' 202 | transformed_string = transform(orig_string) 203 | 204 | orig = sorted(transformed_string) 205 | FINAL_ANAGRAM_LEN = len(orig) 206 | solution = set() 207 | 208 | recurse(orig, [], solution, len(orig), 0) 209 | print('After permuting the anagram %s has %s combinations' % 210 | (orig_string, len(solution))) 211 | 212 | 213 | common_ngrams_dict = {} 214 | # now we need to build a map of the english language's most common bigrams, common bigrams 215 | # are referred to as collocations - two words that, together, are unusually common in 216 | # english texts. we use this technique to filter out the nonsense anagram combinations 217 | # to a sane number this file has just over 1M trigrams, about 17MB (that's fine) 218 | with open(INPUT_TRIGRAMS, 'r') as ngrams_data: 219 | clean_lines = [s.strip() for s in ngrams_data.read().splitlines()] 220 | for line in clean_lines: 221 | freq, w1, w2, w3 = line.split() 222 | common_ngrams_dict[(w1, w2, w3)] = int(freq) 223 | 224 | 225 | print('ngram dictionary contains %s words' % len(common_ngrams_dict)) 226 | 227 | filtered_solution = [] 228 | for anagram in solution: 229 | trigram = tuple(anagram.split()) 230 | if trigram in common_ngrams_dict: 231 | filtered_solution.append(anagram) 232 | 233 | 234 | print('After filtering anagrams without common trigrams, the anagram %s has %s combinations' % 235 | (orig_string, len(filtered_solution))) 236 | 237 | 238 | for anagram in filtered_solution: 239 | print(anagram.upper()) 240 | ``` 241 | 242 | Iterating all 1,458 anagram candidates and merely checking if they existed in the most frequent trigrams hashmap yielded exactly one result!!! 243 | 244 | ![](./davinci5.png) 245 | 246 | LEONARDO DA VINCI is the anagram. The grail puzzle is solved, for now at least! 247 | 248 | "O, DRACONIAN DEVIL" <==> "LEONARDO DA VINCI" 249 | 250 | *Robert and Sophie now sprint over to the Mona Lisa in the movie, they move on to the next puzzle!* 251 | 252 | ![](./davinci6.jpg) 253 | 254 | -------------------------------------------------------------------------------- /google-10000-english-usa.txt: -------------------------------------------------------------------------------- 1 | the 2 | of 3 | and 4 | to 5 | a 6 | in 7 | for 8 | is 9 | on 10 | that 11 | by 12 | this 13 | with 14 | i 15 | you 16 | it 17 | not 18 | or 19 | be 20 | are 21 | from 22 | at 23 | as 24 | your 25 | all 26 | have 27 | new 28 | more 29 | an 30 | was 31 | we 32 | will 33 | home 34 | can 35 | us 36 | about 37 | if 38 | page 39 | my 40 | has 41 | search 42 | free 43 | but 44 | our 45 | one 46 | other 47 | do 48 | no 49 | information 50 | time 51 | they 52 | site 53 | he 54 | up 55 | may 56 | what 57 | which 58 | their 59 | news 60 | out 61 | use 62 | any 63 | there 64 | see 65 | only 66 | so 67 | his 68 | when 69 | contact 70 | here 71 | business 72 | who 73 | web 74 | also 75 | now 76 | help 77 | get 78 | pm 79 | view 80 | online 81 | c 82 | e 83 | first 84 | am 85 | been 86 | would 87 | how 88 | were 89 | me 90 | s 91 | services 92 | some 93 | these 94 | click 95 | its 96 | like 97 | service 98 | x 99 | than 100 | find 101 | price 102 | date 103 | back 104 | top 105 | people 106 | had 107 | list 108 | name 109 | just 110 | over 111 | state 112 | year 113 | day 114 | into 115 | email 116 | two 117 | health 118 | n 119 | world 120 | re 121 | next 122 | used 123 | go 124 | b 125 | work 126 | last 127 | most 128 | products 129 | music 130 | buy 131 | data 132 | make 133 | them 134 | should 135 | product 136 | system 137 | post 138 | her 139 | city 140 | t 141 | add 142 | policy 143 | number 144 | such 145 | please 146 | available 147 | copyright 148 | support 149 | message 150 | after 151 | best 152 | software 153 | then 154 | jan 155 | good 156 | video 157 | well 158 | d 159 | where 160 | info 161 | rights 162 | public 163 | books 164 | high 165 | school 166 | through 167 | m 168 | each 169 | links 170 | she 171 | review 172 | years 173 | order 174 | very 175 | privacy 176 | book 177 | items 178 | company 179 | r 180 | read 181 | group 182 | sex 183 | need 184 | many 185 | user 186 | said 187 | de 188 | does 189 | set 190 | under 191 | general 192 | research 193 | university 194 | january 195 | mail 196 | full 197 | map 198 | reviews 199 | program 200 | life 201 | know 202 | games 203 | way 204 | days 205 | management 206 | p 207 | part 208 | could 209 | great 210 | united 211 | hotel 212 | real 213 | f 214 | item 215 | international 216 | center 217 | ebay 218 | must 219 | store 220 | travel 221 | comments 222 | made 223 | development 224 | report 225 | off 226 | member 227 | details 228 | line 229 | terms 230 | before 231 | hotels 232 | did 233 | send 234 | right 235 | type 236 | because 237 | local 238 | those 239 | using 240 | results 241 | office 242 | education 243 | national 244 | car 245 | design 246 | take 247 | posted 248 | internet 249 | address 250 | community 251 | within 252 | states 253 | area 254 | want 255 | phone 256 | dvd 257 | shipping 258 | reserved 259 | subject 260 | between 261 | forum 262 | family 263 | l 264 | long 265 | based 266 | w 267 | code 268 | show 269 | o 270 | even 271 | black 272 | check 273 | special 274 | prices 275 | website 276 | index 277 | being 278 | women 279 | much 280 | sign 281 | file 282 | link 283 | open 284 | today 285 | technology 286 | south 287 | case 288 | project 289 | same 290 | pages 291 | uk 292 | version 293 | section 294 | own 295 | found 296 | sports 297 | house 298 | related 299 | security 300 | both 301 | g 302 | county 303 | american 304 | photo 305 | game 306 | members 307 | power 308 | while 309 | care 310 | network 311 | down 312 | computer 313 | systems 314 | three 315 | total 316 | place 317 | end 318 | following 319 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