├── .gitignore ├── LICENSE ├── Makefile ├── README.md ├── miknaaz ├── __init__.py ├── corpus_builder.py └── templater.py ├── requirments.txt ├── setup.py └── tests ├── samples └── yusri.csv ├── test_corpus_builder.py └── tokenize_uniq2.sh /.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 | build/ 12 | develop-eggs/ 13 | dist/ 14 | downloads/ 15 | eggs/ 16 | .eggs/ 17 | lib/ 18 | lib64/ 19 | parts/ 20 | sdist/ 21 | var/ 22 | wheels/ 23 | *.egg-info/ 24 | .installed.cfg 25 | *.egg 26 | MANIFEST 27 | 28 | # PyInstaller 29 | # Usually these files are written by a python script from a template 30 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 31 | *.manifest 32 | *.spec 33 | 34 | # Installer logs 35 | pip-log.txt 36 | pip-delete-this-directory.txt 37 | 38 | # Unit test / coverage reports 39 | htmlcov/ 40 | .tox/ 41 | .coverage 42 | .coverage.* 43 | .cache 44 | nosetests.xml 45 | coverage.xml 46 | *.cover 47 | .hypothesis/ 48 | .pytest_cache/ 49 | 50 | # Translations 51 | *.mo 52 | *.pot 53 | 54 | # Django stuff: 55 | *.log 56 | local_settings.py 57 | db.sqlite3 58 | 59 | # Flask stuff: 60 | instance/ 61 | .webassets-cache 62 | 63 | # Scrapy stuff: 64 | .scrapy 65 | 66 | # Sphinx documentation 67 | docs/_build/ 68 | 69 | # PyBuilder 70 | target/ 71 | 72 | # Jupyter Notebook 73 | .ipynb_checkpoints 74 | 75 | # pyenv 76 | .python-version 77 | 78 | # celery beat schedule file 79 | celerybeat-schedule 80 | 81 | # SageMath parsed files 82 | *.sage.py 83 | 84 | # Environments 85 | .env 86 | .venv 87 | env/ 88 | venv/ 89 | ENV/ 90 | env.bak/ 91 | venv.bak/ 92 | 93 | # Spyder project settings 94 | .spyderproject 95 | .spyproject 96 | 97 | # Rope project settings 98 | .ropeproject 99 | 100 | # mkdocs documentation 101 | /site 102 | 103 | # mypy 104 | .mypy_cache/ 105 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 3, 29 June 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU General Public License is a free, copyleft license for 11 | software and other kinds of works. 12 | 13 | The licenses for most software and other practical works are designed 14 | to take away your freedom to share and change the works. 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It is safest 630 | to attach them to the start of each source file to most effectively 631 | state the exclusion of warranty; and each file should have at least 632 | the "copyright" line and a pointer to where the full notice is found. 633 | 634 | 635 | Copyright (C) 636 | 637 | This program is free software: you can redistribute it and/or modify 638 | it under the terms of the GNU General Public License as published by 639 | the Free Software Foundation, either version 3 of the License, or 640 | (at your option) any later version. 641 | 642 | This program is distributed in the hope that it will be useful, 643 | but WITHOUT ANY WARRANTY; without even the implied warranty of 644 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 645 | GNU General Public License for more details. 646 | 647 | You should have received a copy of the GNU General Public License 648 | along with this program. If not, see . 649 | 650 | Also add information on how to contact you by electronic and paper mail. 651 | 652 | If the program does terminal interaction, make it output a short 653 | notice like this when it starts in an interactive mode: 654 | 655 | Copyright (C) 656 | This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. 657 | This is free software, and you are welcome to redistribute it 658 | under certain conditions; type `show c' for details. 659 | 660 | The hypothetical commands `show w' and `show c' should show the appropriate 661 | parts of the General Public License. Of course, your program's commands 662 | might be different; for a GUI interface, you would use an "about box". 663 | 664 | You should also get your employer (if you work as a programmer) or school, 665 | if any, to sign a "copyright disclaimer" for the program, if necessary. 666 | For more information on this, and how to apply and follow the GNU GPL, see 667 | . 668 | 669 | The GNU General Public License does not permit incorporating your program 670 | into proprietary programs. If your program is a subroutine library, you 671 | may consider it more useful to permit linking proprietary applications with 672 | the library. If this is what you want to do, use the GNU Lesser General 673 | Public License instead of this License. But first, please read 674 | . 675 | -------------------------------------------------------------------------------- /Makefile: -------------------------------------------------------------------------------- 1 | #/usr/bin/sh 2 | # Build arabic-roots package 3 | 4 | default: all 5 | # Clean build files 6 | clean: 7 | 8 | backup: 9 | 10 | #create all files 11 | all: 12 | 13 | # convert roots.txt into programming language tables: 14 | #python 15 | build:DATA=abdnormal 16 | build: 17 | echo "Tokenize sample data texts and extract unique words" 18 | scripts/tokenize_uniq2.sh samples/${DATA}.txt > output/${DATA}.unq 19 | python3 scripts/analyze_doc.py -f output/${DATA}.unq -o output/${DATA}.csv 20 | echo "Data is stored in output/${DATA}.csv" 21 | echo "Data stats are stored in output/${DATA}.csv.stats" 22 | 23 | words:DATA=words 24 | words: 25 | echo "Tokenize sample data word list and extract unique words" 26 | cut -f1 samples/${DATA}.csv > output/${DATA}.unq 27 | python3 scripts/analyze_doc.py -f output/${DATA}.unq -o output/${DATA}.csv 28 | echo "Data is stored in output/${DATA}.csv" 29 | echo "Data stats are stored in output/${DATA}.csv.stats" 30 | 31 | 32 | rostom:DATA=rostomya-utf 33 | rostom: 34 | echo "Tokenize sample data word list and extract unique words" 35 | cut -d\; -f1 samples/${DATA}.csv > output/${DATA}.unq 36 | python3 scripts/analyze_doc_rostomya.py -f output/${DATA}.unq -o output/${DATA}.csv 37 | echo "Data is stored in output/${DATA}.csv" 38 | echo "Data stats are stored in output/${DATA}.csv.stats" 39 | 40 | # Publish to github 41 | publish: 42 | git push origin master 43 | 44 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Miknaaz مكناز 2 | 3 | ## Description 4 | 5 | Generate Arabic golden standard corpus for morphology and stemming 6 | 7 | ### Citation 8 | 9 | If you would cite it in academic work, can you use this citation 10 | 11 | Taha Zerrouki‏, Miknaaz, http://github.com/linuxscout/miknaaz, 2023 12 | 13 | or in bibtex format 14 | 15 | @misc{zerrouki2018miknaaz, 16 | title={Miknaaz: Generate arabic golden standard}, 17 | author={Zerrouki, Taha}, 18 | url={http://github.com/linuxscout/miknaaz}, 19 | year={2018} 20 | } 21 | 22 | ## Usage 23 | 24 | * Build word features for linguistics building corpus 25 | 26 | ```python 27 | from miknaaz.corpus_builder import CorpusBuilder 28 | text = u"إلى البيت" 29 | lemmer = CorpusBuilder() 30 | words = lemmer.tokenize(text) 31 | for word in words: 32 | result = lemmer.morph_suggestions(word, True) 33 | print(result) 34 | ``` 35 | 36 | 37 | 38 | * Extract separate features 39 | 40 | ```python 41 | from miknaaz.corpus_builder import CorpusBuilder 42 | text = u"إلى البيت" 43 | lemmer = CorpusBuilder() 44 | words = lemmer.tokenize(text) 45 | # test get lemmas 46 | for word in words: 47 | result = lemmer.get_lemmas(word) 48 | # the result contains objects 49 | print(result) 50 | # test get roots 51 | for word in words: 52 | result = lemmer.get_roots(word) 53 | # the result contains objects 54 | print(result) 55 | # test get wordtypes 56 | for word in words: 57 | result = lemmer.get_word_type(word) 58 | # the result contains objects 59 | print(result) 60 | # test get wazns 61 | for word in words: 62 | result = lemmer.get_wazns(word) 63 | # the result contains objects 64 | print(result) 65 | ``` 66 | 67 | 68 | -------------------------------------------------------------------------------- /miknaaz/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/linuxscout/miknaaz/8a1fa7884a6db97d6c4334fc18950532ce6f6869/miknaaz/__init__.py -------------------------------------------------------------------------------- /miknaaz/corpus_builder.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/python 2 | # -*- coding=utf-8 -*- 3 | #------------------------------------------------------------------------ 4 | # Name: lemmatizer 5 | # Purpose: Arabic Lemmatizer 6 | # 7 | # Author: Taha Zerrouki (taha.zerrouki[at]gmail.com) 8 | # 9 | # Created: 26-08-2020 10 | # Copyright: (c) Taha Zerrouki 2011 11 | # Licence: GPL 12 | #------------------------------------------------------------------------ 13 | """ 14 | Syntaxic Analysis 15 | """ 16 | 17 | #~ from operator import xor 18 | #~ import functools 19 | #~ import operator 20 | import pprint 21 | 22 | import pyarabic.araby as araby 23 | from qalsadi import lemmatizer 24 | try: 25 | import templater 26 | except: 27 | from . import templater 28 | 29 | class CorpusBuilder(lemmatizer.Lemmatizer): 30 | """ 31 | Arabic Lemmatizer 32 | """ 33 | def __init__(self, cache_path=False): 34 | """ 35 | """ 36 | # init corpus builder 37 | super().__init__() 38 | 39 | # set vocalized lemma generation 40 | self.set_vocalized_lemma() 41 | # create analexer 42 | self.templater = templater.Templater() 43 | """Templater to extract wazns for words""" 44 | self.separator =";" 45 | """seperator between list of lemmas or roots""" 46 | self.join_flag = True 47 | """ A flag to join results as texts or return the as lists""" 48 | 49 | 50 | 51 | def __del__(self): 52 | pass 53 | 54 | @staticmethod 55 | def flatten(mylist, uniq=True): 56 | """ 57 | used to flatten list of list into a list 58 | """ 59 | newlist = [item for sublist in mylist for item in sublist] 60 | newlist = sorted(list(set(newlist))) 61 | return newlist 62 | 63 | def join(self, mylist): 64 | """ 65 | return a string for given list 66 | """ 67 | return self.separator.join(mylist) 68 | 69 | def set_join_flag(self, value): 70 | """ set A flag to join results as texts or return the as lists""" 71 | 72 | self.join_flag = bool(value) 73 | 74 | def set_sep(self, sep=";"): 75 | """Specifiy the separator to join list of lemmas or roots""" 76 | self.separator = sep 77 | 78 | def morph_suggestions(self, word, to_string= True): 79 | """ 80 | Generate lemmas suggestions for word, 81 | This can help linguist to choose a suitable words features (lemmas, roots, types, wazns) 82 | 83 | Example: 84 | 85 | >>> text = u"إلى البيت" 86 | >>> result = [] 87 | >>> lemmer = CorpusBuilder() 88 | >>> words = lemmer.tokenize(text) 89 | >>> for word in words: 90 | >>> result = lemmer.morph_suggestions(word, True) 91 | >>> print(result) 92 | {'affixes': ['---'], 93 | 'lemmas': ['إلى'], 94 | 'roots': [''], 95 | 'types': ['stopword'], 96 | 'wazns': []} 97 | {'affixes': ['ال---'], 98 | 'lemmas': ['بيت'], 99 | 'roots': ['بيت'], 100 | 'types': ['noun'], 101 | 'wazns': ['فعل']} 102 | 103 | """ 104 | # first analyse text 105 | stemnodes = self.analyze_text(text=word, vocalized_lemma = True) 106 | 107 | # The analysis give us stem nodes, 108 | # return lemmas 109 | lemmas = [s.get_lemmas() for s in stemnodes] 110 | lemmas = self.flatten(lemmas) 111 | 112 | roots = [s.get_roots() for s in stemnodes] 113 | roots = self.flatten(roots) 114 | 115 | affixes = [s.get_affixes() for s in stemnodes] 116 | # flatten affixes list 117 | affixes = self.flatten(affixes) 118 | 119 | # strip tashkeel 120 | affixes = [araby.strip_tashkeel(item) for item in affixes] 121 | # make uniq 122 | affixes = list(set(affixes)) 123 | types = [s.get_word_type() for s in stemnodes] 124 | 125 | morph_list = [(s.get_lemmas(), s.get_roots(), s.get_word_type()) for s in stemnodes] 126 | wazns = self.templater.extract_wazns(morph_list) 127 | 128 | result = {"lemmas":lemmas, 129 | "roots":roots, 130 | "affixes":affixes, 131 | "types":types, 132 | "wazns":wazns, 133 | } 134 | if to_string: 135 | for key in result: 136 | result[key] = self.join(result[key]) 137 | 138 | 139 | 140 | return result 141 | 142 | def get_roots(self, word): 143 | """ 144 | get all lemmas for word 145 | """ 146 | stemnodes = self.analyze_text(text=word, vocalized_lemma = True) 147 | roots = [s.get_roots() for s in stemnodes] 148 | return roots 149 | 150 | def get_lemmas(self, word): 151 | """ 152 | get all lemmas for word 153 | """ 154 | stemnodes = self.analyze_text(text=word, vocalized_lemma = True) 155 | lemmas = [s.get_lemmas() for s in stemnodes] 156 | return lemmas 157 | 158 | def get_roots(self, word): 159 | """ 160 | get all roots for word 161 | """ 162 | stemnodes = self.analyze_text(text=word, vocalized_lemma = True) 163 | roots = [s.get_roots() for s in stemnodes] 164 | 165 | return roots 166 | 167 | def get_word_type(self, word): 168 | """ 169 | get all lemmas for word 170 | """ 171 | stemnodes = self.analyze_text(text=word, vocalized_lemma = True) 172 | types = [s.get_word_type() for s in stemnodes] 173 | return types 174 | 175 | def get_affixes(self, word): 176 | """ 177 | get all lemmas for word 178 | """ 179 | stemnodes = self.analyze_text(text=word, vocalized_lemma = True) 180 | affixes = [s.get_affixes() for s in stemnodes] 181 | # flatten affixes list 182 | affixes = self.flatten(affixes) 183 | # strip tashkeel 184 | affixes = [araby.strip_tashkeel(item) for item in affixes] 185 | # make uniq 186 | affixes = list(set(affixes)) 187 | return affixes 188 | 189 | def get_wazns(self, word): 190 | """ 191 | get all lemmas for word 192 | """ 193 | stemnodes = self.analyze_text(text=word, vocalized_lemma = True) 194 | lemmas_roots_list = [(s.get_lemmas(), s.get_roots(), s.get_word_type()) for s in stemnodes] 195 | wazns = self.templater.extract_wazns(lemmas_roots_list) 196 | return wazns 197 | 198 | def get_wazns(self, word): 199 | """ 200 | get all lemmas for word 201 | """ 202 | stemnodes = self.analyze_text(text=word, vocalized_lemma = True) 203 | lemmas_roots_list = [(s.get_lemmas(), s.get_roots(), s.get_word_type()) for s in stemnodes] 204 | wazns = self.templater.extract_wazns(lemmas_roots_list) 205 | return wazns 206 | 207 | def tokenize(self, text): 208 | """ retrun tokens from given text""" 209 | return araby.tokenize(text) 210 | 211 | 212 | 213 | def mainly(): 214 | """ 215 | main test 216 | """ 217 | # #test syn 218 | result = [] 219 | text = u"إلى البيت" 220 | lemmer = CorpusBuilder() 221 | words = lemmer.tokenize(text) 222 | for word in words: 223 | result = lemmer.morph_suggestions(word, True) 224 | # the result contains objects 225 | pprint.pprint(result) 226 | # test get lemmas 227 | for word in words: 228 | result = lemmer.get_lemmas(word) 229 | # the result contains objects 230 | print(result) 231 | # test get roots 232 | for word in words: 233 | result = lemmer.get_roots(word) 234 | # the result contains objects 235 | print(result) 236 | # test get wordtypes 237 | for word in words: 238 | result = lemmer.get_word_type(word) 239 | # the result contains objects 240 | print(result) 241 | # test get wazns 242 | for word in words: 243 | result = lemmer.get_wazns(word) 244 | # the result contains objects 245 | print(result) 246 | 247 | if __name__ == "__main__": 248 | mainly() 249 | 250 | -------------------------------------------------------------------------------- /miknaaz/templater.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/python 2 | # -*- coding=utf-8 -*- 3 | #------------------------------------------------------------------------ 4 | # Name: lemmatizer 5 | # Purpose: Arabic Lemmatizer 6 | # 7 | # Author: Taha Zerrouki (taha.zerrouki[at]gmail.com) 8 | # 9 | # Created: 26-08-2020 10 | # Copyright: (c) Taha Zerrouki 2011 11 | # Licence: GPL 12 | #------------------------------------------------------------------------ 13 | """ 14 | Syntaxic Analysis 15 | """ 16 | 17 | #~ from operator import xor 18 | #~ import functools 19 | #~ import operator 20 | import pprint 21 | import re 22 | import pyarabic.araby as araby 23 | 24 | class Templater: 25 | """ 26 | Arabic Templater 27 | """ 28 | def __init__(self, cache_path=False): 29 | """ 30 | """ 31 | # create analexer 32 | pass 33 | 34 | def extract_wazns(self, lemmas_roots_list): 35 | """ 36 | Extract a list of wazns 37 | """ 38 | wazns = [] 39 | for lemmas, roots,word_type in lemmas_roots_list: 40 | if word_type == "stopword": 41 | continue 42 | lemmas = list(set(lemmas)) 43 | roots = list(set(roots)) 44 | for lemma in lemmas: 45 | 46 | for root in roots: 47 | # uniq 48 | wazns.append(self.extract_wazn(lemma, root)) 49 | wazns = list(set(wazns)) 50 | return wazns 51 | @staticmethod 52 | def is_invalid_wazn(wazn): 53 | """ 54 | Test if the wazn is valid 55 | test invalid letters in a wazn, before make it a template 56 | here F, Ain, Lam, are coded 0,1,2 57 | """ 58 | # حروف غير اشتقاقية 59 | if bool(re.search(r"[بثجحخذرزسشصضظعفقكلهء]",wazn)): 60 | return True 61 | # if the numbers are missplaced 62 | if bool(re.search(r"[^0][دط]",wazn)): 63 | return True 64 | # if the numbers are missplaced 65 | if bool(re.search(r"[12].*0|2.*1",wazn)): 66 | return True 67 | return False 68 | 69 | def extract_wazn(self, lemma, root): 70 | """ 71 | extract a wazn from a given lemma and root 72 | """ 73 | if not root or len(root)<3: 74 | return "" 75 | else: 76 | wazn = lemma[::-1] 77 | if len(root) == 4: 78 | wazn = wazn.replace(root[2], "2",2) 79 | else: 80 | # doubled root 81 | if root[2]==root[1]: 82 | wazn = wazn.replace(root[2], "1",1) 83 | else: 84 | wazn = wazn.replace(root[2], "2",1) 85 | wazn = wazn.replace(root[1], "1",1) 86 | wazn = wazn.replace(root[0], "0",1) 87 | wazn = wazn[::-1] 88 | if self.is_invalid_wazn(wazn): 89 | return "" 90 | wazn = wazn.replace("2","ل") 91 | wazn = wazn.replace("1","ع") 92 | wazn = wazn.replace("0","ف") 93 | return wazn 94 | 95 | 96 | 97 | 98 | def mainly(): 99 | """ 100 | main test 101 | """ 102 | # #test syn 103 | text = u"إلى البيت" 104 | result = [] 105 | lemmer = Lemmatizer() 106 | result = lemmer.analyze_text(text) 107 | # the result contains objects 108 | pprint.pprint(result) 109 | 110 | if __name__ == "__main__": 111 | mainly() 112 | 113 | -------------------------------------------------------------------------------- /requirments.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/linuxscout/miknaaz/8a1fa7884a6db97d6c4334fc18950532ce6f6869/requirments.txt -------------------------------------------------------------------------------- /setup.py: -------------------------------------------------------------------------------- 1 | #! /usr/bin/python 2 | from setuptools import setup 3 | from io import open 4 | # to install type: 5 | # python setup.py install --root=/ 6 | def readme(): 7 | with open('README.rst', encoding='utf8') as f: 8 | return f.read() 9 | 10 | setup (name='miknaaz', version='0.1', 11 | description='Miknaaz Arabic morphological corpus builder', 12 | long_description = readme(), 13 | author='Taha Zerrouki', 14 | author_email='taha.zerrouki@gmail .com', 15 | url='http://miknaaz.sourceforge.net/', 16 | license='GPL', 17 | package_dir={'miknaaz': 'miknaaz',}, 18 | packages=['miknaaz'], 19 | include_package_data=True, 20 | install_requires=[ "six>=1.10.0", 21 | "future>=0.16.0", 22 | "pickledb>=0.9.2", 23 | "qalsadi>=0.4.6", 24 | "alyahmor>=0.1", 25 | "libqutrub>=1.2.3", 26 | "naftawayh>=0.3", 27 | "pyarabic>=0.6.7", 28 | "tashaphyne>=0.3.4.1", 29 | "arramooz-pysqlite>=0.3", 30 | "Arabic-Stopwords>=0.3", 31 | ], 32 | package_data = { 33 | 'miknaaz': ['doc/*.*','doc/html/*', 'data/*.*', 'miknaaz/data'], 34 | }, 35 | classifiers=[ 36 | 'Development Status :: 5 - Production/Stable', 37 | 'Natural Language :: Arabic', 38 | 'Intended Audience :: Developers', 39 | 'Operating System :: OS Independent', 40 | 'Programming Language :: Python', 41 | 'Topic :: Text Processing :: Linguistic', 42 | ], 43 | ); 44 | 45 | -------------------------------------------------------------------------------- /tests/samples/yusri.csv: -------------------------------------------------------------------------------- 1 | word freq 2 | في 12 3 | من 7 4 | مع 6 5 | هو 4 6 | على 4 7 | الماء 4 8 | إلى 3 9 | هناك 2 10 | مخيمات 2 11 | كنت 2 12 | كنا 2 13 | كما 2 14 | كل 2 15 | كتبت 2 16 | عندنا 2 17 | عندما 2 18 | عن 2 19 | صناعة 2 20 | ذلك 2 21 | حق 2 22 | ثم 2 23 | تلك 2 24 | تعود 2 25 | بل 2 26 | بعض 2 27 | الوزارة 2 28 | الشباب 2 29 | الحال 2 30 | أي 2 31 | أو 2 32 | أقطن 2 33 | أبو 2 34 | يومية 1 35 | يوم 1 36 | يمنع 1 37 | يمكن 1 38 | يستطيع 1 39 | يدها 1 40 | يبدو 1 41 | وندوات 1 42 | ونجحت 1 43 | ومواقف 1 44 | ومؤثر 1 45 | ولم 1 46 | ولكنه 1 47 | وكيل 1 48 | وكمال 1 49 | وكان 1 50 | وقد 1 51 | وفي 1 52 | وعربي 1 53 | وضعت 1 54 | وصولاً 1 55 | ورشات 1 56 | ورئيسهابما 1 57 | ودولي 1 58 | وجمع 1 59 | وبمتابعة 1 60 | وبإشراف 1 61 | واليوم 1 62 | والهاتف 1 63 | والمكان 1 64 | والغاز 1 65 | والعصافير 1 66 | والرياضة 1 67 | والخطيب 1 68 | والأنترت 1 69 | وإن 1 70 | وأنا 1 71 | وأفلام 1 72 | وأذكر 1 73 | وأبو 1 74 | وآخرين 1 75 | هوليود 1 76 | همام 1 77 | هل 1 78 | هذا 1 79 | ننتقل 1 80 | نعيش 1 81 | نستيقظ 1 82 | نسأل 1 83 | نجحت 1 84 | نتائج 1 85 | موضوع 1 86 | مواطن 1 87 | موارد 1 88 | منها 1 89 | منع 1 90 | منزلي 1 91 | مما 1 92 | مقدساتنا 1 93 | مقدس 1 94 | مغلقة 1 95 | معسكر 1 96 | ـمع 1 97 | مشروع 1 98 | مدراء 1 99 | مختلفة 1 100 | محمد 1 101 | محلي 1 102 | مجمعة 1 103 | مجالات 1 104 | ماءاً 1 105 | ماء 1 106 | ما 1 107 | مؤثرين 1 108 | ليتها 1 109 | لمربع 1 110 | للشرب 1 111 | لكن 1 112 | لفيروز 1 113 | لشركة 1 114 | لافتات 1 115 | لا 1 116 | كثيراً 1 117 | كثيرا 1 118 | كانت 1 119 | كان 1 120 | قطعه 1 121 | قضية 1 122 | قد 1 123 | قائمة 1 124 | فيه 1 125 | فيروز 1 126 | فوق 1 127 | فما 1 128 | فلسطين 1 129 | فايز 1 130 | فالله 1 131 | فالخطبة 1 132 | غيره 1 133 | غريبا 1 134 | غالي 1 135 | غابت 1 136 | عمل 1 137 | عليها 1 138 | علم 1 139 | عرفة 1 140 | عرفات 1 141 | عدة 1 142 | عبر 1 143 | عام 1 144 | طلعت 1 145 | طاهر 1 146 | طاقم 1 147 | صوت 1 148 | شيخ 1 149 | شعور 1 150 | شركة 1 151 | شركات 1 152 | شخصياتنا 1 153 | شخصيات 1 154 | شتى 1 155 | شبابية 1 156 | شؤون 1 157 | سيال 1 158 | سماحة 1 159 | سعوديا 1 160 | سؤال 1 161 | روايات 1 162 | رغم 1 163 | راضي 1 164 | رؤى 1 165 | رأسي 1 166 | دورة 1 167 | دفع 1 168 | دستورها 1 169 | خير 1 170 | خوصصته 1 171 | خلال 1 172 | خطبة 1 173 | خصخصته 1 174 | خاصة 1 175 | حين 1 176 | حيث 1 177 | حقيقي 1 178 | حقوق 1 179 | حديقة 1 180 | حتى 1 181 | جيد 1 182 | جوفاء 1 183 | جعلني 1 184 | جدا 1 185 | جازما 1 186 | ثمن 1 187 | تم 1 188 | تقديري 1 189 | تغني 1 190 | تسييره 1 191 | تحمل 1 192 | تجنبت 1 193 | تجرح 1 194 | تتكفل 1 195 | تتباهى 1 196 | تبدأ 1 197 | بين 1 198 | بي 1 199 | بند 1 200 | بمهام 1 201 | بلدان 1 202 | بلجيكا 1 203 | بقيادة 1 204 | بصناعة 1 205 | بصقل 1 206 | بصفاء 1 207 | بشكل 1 208 | بخل 1 209 | بال 1 210 | باريس 1 211 | اليازجي 1 212 | الولايات 1 213 | النفايات 1 214 | المياه 1 215 | الموضوعات 1 216 | المشاركين 1 217 | المسلمين 1 218 | المخيمات 1 219 | المحلية 1 220 | المتحدة 1 221 | المافيا 1 222 | اللطف 1 223 | اللحن 1 224 | الكهرباء 1 225 | الكشافة 1 226 | القيادة 1 227 | القهوة 1 228 | القلم 1 229 | القانون 1 230 | القادة 1 231 | الفرنسية 1 232 | الفرا 1 233 | العالمية 1 234 | الصديق 1 235 | الصالح 1 236 | الشمس 1 237 | الشمالية 1 238 | الشركة 1 239 | الشديد 1 240 | السؤال 1 241 | الزمن 1 242 | الذاكرة 1 243 | الدول 1 244 | الدعاء 1 245 | الدستور 1 246 | الدائم 1 247 | الخيال 1 248 | الخطيب 1 249 | الخدمة 1 250 | الحياة 1 251 | الحنفية 1 252 | الحقيقية 1 253 | الحصول 1 254 | الحدود 1 255 | الجنوبية 1 256 | التي 1 257 | البيت 1 258 | البترول 1 259 | الاختلافات 1 260 | الإنسان 1 261 | الأيديولوجية 1 262 | الأوروبية 1 263 | الأمريكي 1 264 | الأقل 1 265 | الأسف 1 266 | الأبيض 1 267 | إن 1 268 | إشهار 1 269 | إسناد 1 270 | إدراج 1 271 | أيام 1 272 | أنها 1 273 | أن 1 274 | أمريكا 1 275 | أكن 1 276 | أعتقد 1 277 | أشرب 1 278 | أشخاص 1 279 | أسماؤهم 1 280 | أستمع 1 281 | أذكر 1 282 | أدرجت 1 283 | أخباراً 1 284 | أحمد 1 285 | أحتسي 1 286 | أحببناهم 1 287 | أجلس 1 288 | أتساءل 1 289 | آل 1 290 | آبار 1 291 | -------------------------------------------------------------------------------- /tests/test_corpus_builder.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/python 2 | # -*- coding=utf-8 -*- 3 | #----------------------------------------------------------------------- 4 | # Name: Build a corpus to test and val stemmers and morphological analyzer 5 | # Purpose: build an advanced stemmer for Information retreival 6 | # 7 | # 8 | # Author: Taha Zerrouki (taha.zerrouki[at]gmail.com) 9 | # 10 | # Created: 2018-08-25 11 | # Copyright: (c) Taha Zerrouki 2018 12 | # Licence: GPL 13 | #----------------------------------------------------------------------- 14 | """ 15 | Build a corpus for evaluation 16 | """ 17 | 18 | import sys 19 | import re 20 | import argparse 21 | import os 22 | import pandas as pd 23 | import qalsadi.analex 24 | import pyarabic.araby as araby 25 | import numpy as np 26 | 27 | sys.path.append("../") 28 | from miknaaz.corpus_builder import CorpusBuilder 29 | 30 | def grabargs(): 31 | parser = argparse.ArgumentParser(description='Convert Quran Corpus into CSV format.') 32 | # add file name to import and filename to export 33 | 34 | parser.add_argument("-f", dest="filename", required=True, 35 | help="input file to convert", metavar="FILE") 36 | 37 | parser.add_argument("-o", dest="outfile", required=True, 38 | help="Output file to convert", metavar="OUT_FILE") 39 | #~ parser.add_argument("--dir", dest="data_directory", 40 | #~ help="Data directory for other external stemmers results", metavar="data_directory") 41 | 42 | parser.add_argument("--all", type=bool, nargs='?', 43 | const=True, 44 | help="Test all stemmers.") 45 | args = parser.parse_args() 46 | return args 47 | 48 | def calcul_stats(dataframe): 49 | """ 50 | Calculer 51 | """ 52 | #~ df = pd.DataFrame(dataframe) 53 | df = dataframe 54 | #~ # display= data stats 55 | #~ print('********* ROOT ****************') 56 | total = df.shape[0] 57 | stats_list={ 58 | "count":total, 59 | "uniq roots":df['roots'].nunique(), 60 | "uniq lemmas":df['lemmas'].nunique(), 61 | "uniq words":df['word'].nunique(), 62 | "mean words by root":df[['word','roots']].groupby('roots').count().mean(), 63 | # ~ "min words by root":df[['word','root']].groupby('root').count().min(), 64 | "max words by root":df[['word','roots']].groupby('roots').count().max(), 65 | "mean words by lemmas":df[['word','lemmas']].groupby('lemmas').count().mean(), 66 | } 67 | 68 | dstats = pd.DataFrame.from_dict(stats_list, orient='index') 69 | 70 | return dstats 71 | 72 | def main(): 73 | 74 | args =grabargs() 75 | filename = args.filename 76 | outfile = args.outfile 77 | all_stemmers = args.all 78 | lines =[] 79 | # read data 80 | df = pd.read_csv(filename, encoding="utf8", delimiter="\t") 81 | # fill nan 82 | df.fillna("", inplace=True) 83 | print(df.shape) 84 | print(df.head(20)) 85 | 86 | lemmer = CorpusBuilder() 87 | # add word features into each row 88 | df2 = df.merge(df['word'].apply(lambda s: pd.Series(lemmer.morph_suggestions(s))), 89 | left_index=True, right_index=True) 90 | 91 | print(df2) 92 | df2.to_csv(outfile ,sep='\t',encoding='utf8') 93 | x = calcul_stats(df2) 94 | print(x) 95 | x.to_csv(outfile+'.stats',sep='\t',encoding='utf8') 96 | print("Output data is stored in %s"%outfile) 97 | print("Statistics data is stored in %s"%outfile+'.stats') 98 | if __name__ == '__main__': 99 | main() 100 | -------------------------------------------------------------------------------- /tests/tokenize_uniq2.sh: -------------------------------------------------------------------------------- 1 | #!/bin/sh 2 | # shellshal : Arabic shell command tools 3 | # Shell Script for Arabic Language processing 4 | #سكريبتات سطر الأوامر للتعامل مع النصوص العربية 5 | # github.com/linuxscout/shellshal 6 | # by Taha Zerrouki (taha.zerrouki @ gmail.com) 7 | # License: GPL 8 | # Tokenize, sort and caclul frequency all words in a file 9 | # Usage: 10 | # Tokenize words from text and return only sorted uniq words with frequency 11 | # tokenize_uniq.sh filename 12 | #~ tr -s '[*!"#\$%&\(\)\+,\\\.\/:;،؛<=>\?@\[\\\\]^_`\{|\}~][:space:]]' '\n' < $1 | sort | uniq -c | sort -nr 13 | sed 's/[[:punct:][:space:]×،؛]/\n/g' < $1 |sed '/^\s*$/d' | sort | uniq 14 | 15 | --------------------------------------------------------------------------------