def clean(self, strng):
"""
clean a string from unnecessary whitespaces
"""
#if type(strng) == str or type(strng) == unicode:
if type(strng) == str: #python3
strng = araby.strip_tatweel(strng)
return re.sub(u'\s+', ' ', strng).strip()
if type(strng) == list:
l = [re.sub(u'\s+', ' ', s).strip() for s in strng]
return [araby.strip_tatweel(s) for s in l]
else:
return strng
def normalizeText(self, text):
normalized_text = araby.strip_tatweel(text)
normalized_text = araby.strip_tashkeel(normalized_text)
normalized_text = araby.strip_harakat(normalized_text)
normalized_text = araby.normalize_hamza(normalized_text)
return normalized_text
def grouping_letter_diacritics(sentance):
"""Grouping each letter with its diacritics.
Args:
sentance: str
Returns:
[str]: a list of _x_, where _x_ is the letter accompanied with its
diacritics.
Example:
```python
q.grouping_letter_diacritics('إِنَّا أَعْطَيْنَكَ الْكَوْثَرَ')\n
>>> ['إِ', 'نَّ', 'ا', ' ', 'أَ', 'عْ', 'طَ', 'يْ', 'نَ', 'كَ', ' ', 'ا', 'لْ', 'كَ', 'وْ', 'ثَ', 'رَ']
```
"""
sentance_without_tatweel = strip_tatweel(sentance)
print(sentance_without_tatweel)
hroof_with_tashkeel = []
for index, i in enumerate(sentance):
if ((sentance[index] in (alphabet or alefat or hamzat)
or sentance[index] is ' ')):
k = index
harf_with_taskeel = sentance[index]
while ((k + 1) != len(sentance)
and (sentance[k + 1] in (tashkeel or harakat or shortharakat
or tanwin))):
harf_with_taskeel = harf_with_taskeel + "" + sentance[k + 1]
k = k + 1
index = k
hroof_with_tashkeel.append(harf_with_taskeel)
return hroof_with_tashkeel
def lookup(self, word):
"""
Lookup if the word is correct or not
@param word: input word
@type word: unicode
@return: True if word exists else False
@rtype: Boolean
"""
if not word:
return True
if word.isdigit():
return True
for c in word:
if c in string.punctuation:
return True
# test if the word is previouslly spelled
# can get True or False
if word in self.worddict:
test = self.worddict.get(word, False)
else:
# if the word is not spelled
word = araby.strip_tatweel(word)
self.stemmer.segment(word)
# extract the affix
stem = self.stemmer.get_stem()
affix = u"-".join([self.stemmer.get_prefix(), self.stemmer.get_suffix()])
# lookup in the database
test = self.database.lookup(word, stem, affix)
self.worddict[word] = test
return test
def extract_poem():
"""[summary]
might not be using it
Returns:
[type] -- [description]
dict -- postings lists of arabic words
"""
postings_list = {}
tokens = []
with open("short story.txt", encoding='utf-8') as ofile:
for loc, line in enumerate(ofile, 1):
words = araby.tokenize(araby.strip_tashkeel(line))
tokens.extend(words)
for word in words:
if (araby.is_tatweel(word)):
word = araby.strip_tatweel(word)
# if word not in postings_list:
# postings_list[word]=[loc]
# else:
# postings_list[word].append(loc)
#return postings_list
return tokens
def lookup(self, word):
"""
Lookup if the word is correct or not
@param word: input word
@type word: unicode
@return: True if word exists else False
@rtype: Boolean
"""
if not word:
return True
# test if the word is previouslly spelled
# can get True or False
if word in self.worddict:
test = self.worddict.get(word, False)
else:
# if the word is not spelled
word = araby.strip_tatweel(word)
if word.startswith(araby.WAW) or word.startswith(araby.FEH):
conjonction = word[0]
word = word[1:]
else:
conjonction = u""
self.stemmer.segment(word)
# extract the affix
stem = self.stemmer.get_stem()
affix = u"-".join(
[self.stemmer.get_prefix(),
self.stemmer.get_suffix()])
# lookup in the database
test = self.database.lookup(word, stem, affix, conjonction)
self.worddict[word] = test
return test
def lookup(self, word):
"""
Lookup if the word is correct or not
@param word: input word
@type word: unicode
@return: True if word exists else False
@rtype: Boolean
"""
if not word:
return True
# test if the word is previouslly spelled
# can get True or False
if word in self.worddict:
test = self.worddict.get(word, False)
else:
# if the word is not spelled
word = araby.strip_tatweel(word)
self.stemmer.segment(word)
# extract the affix
stem = self.stemmer.get_stem()
affix = u"-".join([self.stemmer.get_prefix(), self.stemmer.get_suffix()])
# lookup in the database
test = self.database.lookup(word, stem, affix)
self.worddict[word] = test
return test
def normalize_arabic(self, text):
text = re.sub(r'\bال(\w\w+)', r'\1', text) # remove al ta3reef
text = re.sub("[إأآاٱ]", "ا", text)
text = re.sub("ى", "ي", text)
text = re.sub("ة", "ه", text) # replace ta2 marboota by ha2
text = re.sub("گ", "ك", text)
text = strip_tatweel(text) #remove tatweel
text = strip_tashkeel(text) #remove tashkeel
return text
def preprocess(sentence):
sentence = araby.strip_tatweel(sentence)
sentence = sentence.replace(
araby.SMALL_ALEF+araby.ALEF_MAKSURA, araby.ALEF_MAKSURA)
sentence = sentence.replace(
araby.ALEF_MAKSURA+araby.SMALL_ALEF, araby.ALEF_MAKSURA)
sentence = re.sub(ALEFAT_PATTERN, araby.ALEF, sentence)
sentence = araby.normalize_ligature(sentence)
sentence = araby.normalize_teh(sentence)
sentence = araby.strip_tashkeel(sentence)
sentence = re.sub(r'[^\d\w]', r' ', sentence)
sentence = re.sub(r'( ){2,}', r'\1', sentence)
return sentence
def word_tagging(self, word_list):
"""
Guess word classification, into verb, noun, stopwords.
return al list of guessed tags
@param word_list: the given word lists.
@type word_list: unicode list.
@return: a tag list : 't': tool, 'v': verb,
'n' :noun, 'nv' or 'vn' unidentifed.
@rtype: unicode list
"""
if len(word_list) == 0:
return []
else:
list_result = []
previous = u""
second_previous = u"" # the second previous
#~ previous_tag = ""
for word in word_list:
word_nm = araby.strip_tashkeel(word)
word_nm = araby.strip_tatweel(word_nm)
tag = ''
if self.cache.has_key(word):
tag = self.cache.get(word, '')
else:
if self.is_stopword(word_nm):
tag = 't'
else:
if self.is_noun(word):
tag += 'n'
if self.is_verb(word):
tag += 'v'
# add the found tag to Cache.
self.cache[word] = tag
# if the tagging give an ambigous tag,
# we can do an contextual analysis
# the contextual tag is not saved in Cache,
# because it can be ambigous.
# for example
# في ضرب : is a noun
# قد ضرب : is a verb
if tag in ("", "vn", "nv"):
tag = self.context_analyse(previous, word) + "3"
if tag in ("", "1", "vn1", "nv1"):
tag = self.context_analyse(
u" ".join([second_previous, previous]), word) + "2"
list_result.append(tag)
second_previous = previous
previous = word_nm
#~ previous_tag = tag
return list_result
def normalize(word, wordtype="affix"):
"""
Normalize the word, by unifoming hamzat, Alef madda, shadda, and lamalefs.
@param word: given word.
@type word: unicode.
@param type: if the word is an affix
@type type: unicode.
@return: converted word.
@rtype: unicode.
"""
# تحويل الكلمة إلى شكلها النظري.
# الشكل اللإملائي للكلمة هو طريقة كتابتها حسب قواعد الإملاء
# الشكل النظري هو الشكل المتخيل للكلمة دون تطبيق قواعد اللغة
# ويخص عادة الأشكال المتعددة للهمزة، و التي تكتب همزة على السطر
# أمثلة
# إملائي نظري
#إِمْلَائِي ءِمْلَاءِي
#سَاَلَ سَءَلَ
# الهدف : تحويل الكلمة إلى شكل نظري،
#ومن ثم إمكانية تصريفها بعيدا عن قواعد الإملاء،
#وبعد التصريف يتم تطبيق قواعد الإملاء من جديد.
#الفرضية: الكلمات المدخلة مشكولة شكلا تاما.
#الطريقة:
# 1-تحويل جميع أنواع الهمزات إلى همزة على السطر
# 1-فك الإدغام
i = 0
# strip tatweel
# the tatweel is used to uniformate the affix
# when the Haraka is used separetely
if wordtype != "affix":
word = araby.strip_tatweel(word)
## تستبدل الألف الممدودة في , ل الكلمة بهمزة قطع بعدها همزة أخرى
if word.startswith(ALEF_MADDA):
word = normalize_alef_madda(word)
# ignore harakat at the begin of the word
len_word = len(word)
while i < len_word and araby.is_shortharaka(word[i]): # in HARAKAT:
i += 1
word = word[i:]
# convert all Hamza from into one form
word = araby.normalize_hamza(word)
#Convert All LAM ALEF Ligature into separate letters
word = word.replace(LAM_ALEF, SIMPLE_LAM_ALEF)
word = word.replace(LAM_ALEF_HAMZA_ABOVE, SIMPLE_LAM_ALEF_HAMZA_ABOVE)
word = word.replace(LAM_ALEF_MADDA_ABOVE, SIMPLE_LAM_ALEF_MADDA_ABOVE)
return word
def create_arabic_node(self, cluster_name, label, **kwargs):
"""
Checks that label is an arabic string, removes tatweel and normalizes
ligatures. Adds unvocalized_label.
"""
label = araby.normalize_ligature(araby.strip_tatweel(label))
label = label.replace(araby.SMALL_ALEF, "")
if not araby.is_arabicstring(label):
raise RuntimeError("'%s' is not an Arabic string" % label)
if "unvocalized_label" not in kwargs:
kwargs["unvocalized_label"] = araby.strip_tashkeel(label)
return self.create_node(cluster_name, label, **kwargs)
def create_arabic_node(self, cluster_name, label, **kwargs):
"""
Checks that label is an arabic string, removes tatweel and normalizes
ligatures. Adds unvocalized_label.
"""
label = araby.normalize_ligature(araby.strip_tatweel(label))
label = label.replace(araby.SMALL_ALEF, "")
if not araby.is_arabicstring(label):
raise RuntimeError("'%s' is not an Arabic string" % label)
if "unvocalized_label" not in kwargs:
kwargs["unvocalized_label"] = araby.strip_tashkeel(label)
return self.create_node(cluster_name, label, **kwargs)
def test_strip(self):
# strip_harakat(text):
assert Araby.strip_harakat(u"الْعَرَبِيّةُ") == u'العربيّة'
# strip_lastharaka(text)
assert Araby.strip_lastharaka(u"الْعَرَبِيّةُ") == u'الْعَرَبِيّة'
# strip_tashkeel(text)
assert Araby.strip_tashkeel(u"الْعَرَبِيّةُ") == u'العربية'
# strip_tatweel(text):
assert Araby.strip_tatweel(u"العـــــربية") == u'العربية'
# strip_shadda(text):
assert Araby.strip_shadda(u"الشّمسيّة") == u'الشمسية'
def normalize(word, wordtype = "affix"):
"""
Normalize the word, by unifoming hamzat, Alef madda, shadda, and lamalefs.
@param word: given word.
@type word: unicode.
@param type: if the word is an affix
@type type: unicode.
@return: converted word.
@rtype: unicode.
"""
# تحويل الكلمة إلى شكلها النظري.
# الشكل اللإملائي للكلمة هو طريقة كتابتها حسب قواعد الإملاء
# الشكل النظري هو الشكل المتخيل للكلمة دون تطبيق قواعد اللغة
# ويخص عادة الأشكال المتعددة للهمزة، و التي تكتب همزة على السطر
# أمثلة
# إملائي نظري
#إِمْلَائِي ءِمْلَاءِي
#سَاَلَ سَءَلَ
# الهدف : تحويل الكلمة إلى شكل نظري،
#ومن ثم إمكانية تصريفها بعيدا عن قواعد الإملاء،
#وبعد التصريف يتم تطبيق قواعد الإملاء من جديد.
#الفرضية: الكلمات المدخلة مشكولة شكلا تاما.
#الطريقة:
# 1-تحويل جميع أنواع الهمزات إلى همزة على السطر
# 1-فك الإدغام
i = 0
# strip tatweel
# the tatweel is used to uniformate the affix
# when the Haraka is used separetely
if wordtype != "affix":
word = araby.strip_tatweel(word)
## تستبدل الألف الممدودة في , ل الكلمة بهمزة قطع بعدها همزة أخرى
if word.startswith(ALEF_MADDA):
word = normalize_alef_madda(word)
# ignore harakat at the begin of the word
len_word = len(word)
while i < len_word and araby.is_shortharaka(word[i]): # in HARAKAT:
i += 1
word = word[i:]
# convert all Hamza from into one form
word = araby.normalize_hamza(word)
#Convert All LAM ALEF Ligature into separate letters
word = word.replace(LAM_ALEF, SIMPLE_LAM_ALEF)
word = word.replace(LAM_ALEF_HAMZA_ABOVE, SIMPLE_LAM_ALEF_HAMZA_ABOVE)
word = word.replace(LAM_ALEF_MADDA_ABOVE, SIMPLE_LAM_ALEF_MADDA_ABOVE)
return word
def preprocess(sentences, stopwords, isStopword = False):
"""
This takes in an array of complete araic sentences, and performs th following operations on all of them:
1.) strips tashkeel
2.) strips harakat
3.) strips lastharaka
4.) strips tatweel
5.) Strips shadda
6.) normalize lam alef ligatures
7.) normalize hamza
8.) tokenize
Returns a 2D martix, where each row represents normalized, tokens of each sentence
"""
#print("SENTENCE INDEX!!!", sentences[0])
output = []
for sentence in sentences:
#print("Before Preprocessing:"+ sentence)
#print(sentence)
text = araby.strip_harakat(sentence)
#print("TEXT!!!!", text)
text = araby.strip_tashkeel(text)
text = araby.strip_lastharaka(text)
text = araby.strip_tatweel(text)
text = araby.strip_shadda(text)
text = araby.normalize_ligature(text)
text = araby.normalize_hamza(text)
text = clean_str(text)
#print("After Preprocessing:"+ text)
#print("----")
#print(text)
try:
text = re.match(r'[^\\n\\s\\p{Latin}]+', text).group()
tokens = araby.tokenize(text)
if not isStopword:
tokens = remove_stopwords(stopwords, tokens)
tokens = [t for t in tokens if t != '\n']
output.append(tokens)
except:
pass
return output
def soundex(sentence):
'''
- blanks and and spaces are deleted,
- long vowels are deleted,
- if two adjacent letters are identical, only one of the two is
kept,
- to each word’s letter are associated two numbers:
1. the first one corresponds to the letter’s main category’s
code. It is represented by an integer N of two bits, such as N
E={0, 1, 2}.
2. the second one corresponds to the letter’s sub-category’s
code. It is represented by an integer n of four bits, such as:
n SE= {0,...,10}.
Thus :
Given a word w, w=w 1 ...w n .
w=w- {blanks and long vowels}=w’ 1 ... w’ n .
f(w)=f(w’)=f(w’ 1 ... w’ n )=N 1 n 1 ... N n n n =X.
The phonetic code generated X, can be used as a hash key
for classifying and indexing purposes.
Returns:
[type] -- [description]
'''
words = araby.tokenize(sentence)
for word in words:
if type(word) != str:
word = word.decode('utf-8')
else:
word = word.encode('utf-8')
loc = 0
for i in word[0:]:
if araby.is_tatweel(i):
word = araby.strip_tatweel(word)
if loc < len(word) and loc != 0 and re.match(
"[\u0627\u064a\u0648]", str(word[loc])):
word = word[:loc] + word[loc + 1:]
if loc < len(word) and re.match("[\u0640]", str(word[loc])):
word = word[:loc] + word[loc + 1:]
loc += 1
print(words)
for key, group in groupby(aa5irHarf)]
print(freqOfAa5irHarf)
import collections
counter = collections.Counter(aa5irHarf)
print(counter)
# Counter({1: 4, 2: 4, 3: 2, 5: 2, 4: 1})
print(counter.values())
# [4, 4, 2, 1, 2]
print(counter.keys())
# [1, 2, 3, 4, 5]
print(counter.most_common(3))
# [(1, 4), (2, 4), (3, 2)]
print(counter.most_common(1))
kkey = counter.most_common(1)
#we should write to file or save it anywhere
#and also we should generalize it to all poems for each poet
#القافية :آخر ساكن وبدور عالساكن اللي قبله مع الحرف المتحرك اللي قبل الساكن ال ما قبل الاخير
print('********** Al Qafiya ************')
for line in f:
line1 = araby.strip_tatweel(line)
letters, hrkat = araby.separate(line1)
#print(letters.encode('utf8'))
for m in hrkat:
#لازم نعمل تعديلات
if not araby.is_tatweel(m):
print(araby.name(m))
print(''.join(m))
#Most Common Words بنعملهم بكل قصائد الشاعر
if araby.is_weak(c): print ('weak'),
if araby.is_moon(c): print ('moon'),
if araby.is_sun(c):print ('sun'),
print (araby.order(c)),
print ();
word=u"الْعَرَيِيّةُ"
word_list=[
u"الْعَرَيِيّةُ",
u"العربية",
u"الْعَرَيِيّةُ الفصحى",
u"غير مشكول",
"Taha",
u"سئل لأنه يؤم الإمام"
]
word1=u""
for word in word_list:
print (word)
if araby.is_vocalized(word): print (' is vocalized')
if araby.is_vocalizedtext(word): print (' is vocalized text')
if araby.is_arabicword(word): print (' is valid word')
else: print ("invalid arabic word")
print (' strip harakat', araby.strip_harakat(word))
print (' strip tashkeel', araby.strip_tashkeel(word))
print (' strip tatweel',araby.strip_tatweel(word))
print (' normalize ligature ', araby.normalize_ligature(word))
print (' normalize hamza', araby.normalize_hamza(word))
if araby.vocalizedlike(word, word1): print ("vocalized_like")
word1=word;
if araby.vocalizedlike(u"العربية",u"العرَبية"): print ("vocalized_like")
tokens.extend(araby.tokenize(line))
for token in tokens:
if len(token) > 1:
soundex(token) """
with io.open("nouns.masdarv2.txt", encoding="utf-8") as doc:
for line in doc:
temptokens.extend(araby.tokenize(line))
for token in temptokens:
if len(token) >= 2 and araby.is_arabicrange(token):
token = token.replace("\u0627", '')
token = token.replace('\u0621', '\u0627')
token = token.replace('\u0621\u0621', '\u0627')
token = token.replace("\u0648", '')
token = token.replace("\u064a", '')
if len(token) >= 2:
tokens.append(
araby.strip_tashkeel(araby.strip_tatweel(token)))
del temptokens
for token in tokens:
soundex(token[::-1])
del tokens
soundex(araby.MEEM + araby.DAL +
araby.KAF) #sample word, backwards because it is processed LTR
soundex(araby.BEH + araby.TEH +
araby.KAF) #sample word, backwards because it is processed LTR
soundex(araby.FEH + araby.TEH +
araby.KAF) #sample word, backwards because it is processed LTR
soundex(araby.FEH + araby.NOON + araby.ALEF_HAMZA_ABOVE
) #sample word, backwards because it is processed LTR
with open("db.json", 'w') as database:
database.write(json.dumps(word_db))
def removeTatweel(self, t):
return strip_tatweel(t)
def word_tagging(self, word_list):
"""
Guess word classification, into verb, noun, stopwords.
return al list of guessed tags
Example:
>>> import naftawayh.wordtag
>>> word_list=(u'بالبلاد', u'بينما', u'أو', u'انسحاب', u'انعدام',
u'انفجار', u'البرنامج', u'بانفعالاتها', u'العربي', u'الصرفي',
u'التطرف', u'اقتصادي', )
>>> tagger = naftawayh.wordtag.WordTagger();
>>> # test all words
>>> list_tags = tagger.word_tagging(word_list)
>>> for word, tag in zip(word_list, list_tags):
>>> print word, tag
بالبلاد n
بينما vn3
أو t
انسحاب n
انعدام n
انفجار n
البرنامج n
بانفعالاتها n
العربي n
الصرفي n
التطرف n
اقتصادي n
@param word_list: the given word lists.
@type word_list: unicode list.
@return: a tag list : 't': tool, 'v': verb,
'n' :noun, 'nv' or 'vn' unidentifed.
@rtype: unicode list
"""
if len(word_list) == 0:
return []
else:
list_result = []
previous = u""
second_previous = u"" # the second previous
#~ previous_tag = ""
for word in word_list:
word_nm = araby.strip_tashkeel(word)
word_nm = araby.strip_tatweel(word_nm)
tag = ''
if self.cache.has_key(word):
tag = self.cache.get(word, '')
else:
if self.is_stopword(word_nm):
tag = 't'
else:
if self.is_noun(word):
tag += 'n'
if self.is_verb(word):
tag += 'v'
# add the found tag to Cache.
self.cache[word] = tag
# if the tagging give an ambigous tag,
# we can do an contextual analysis
# the contextual tag is not saved in Cache,
# because it can be ambigous.
# for example
# في ضرب : is a noun
# قد ضرب : is a verb
if tag in ("", "vn", "nv"):
tag = self.context_analyse(previous, word) + "3"
if tag in ("", "1", "vn1", "nv1"):
tag = self.context_analyse(
u" ".join([second_previous, previous]), word) + "2"
list_result.append(tag)
second_previous = previous
previous = word_nm
#~ previous_tag = tag
return list_result
def _preprocess_v2(self, text: str) -> str:
text = str(text)
text = html.unescape(text)
if self.strip_tashkeel:
text = araby.strip_tashkeel(text)
if self.strip_tatweel:
text = araby.strip_tatweel(text)
if self.replace_urls_emails_mentions:
# replace all possible URLs
for reg in URL_REGEXES:
text = re.sub(reg, " [رابط] ", text)
# REplace Emails with [بريد]
for reg in EMAIL_REGEXES:
text = re.sub(reg, " [بريد] ", text)
# replace mentions with [مستخدم]
text = re.sub(USER_MENTION_REGEX, " [مستخدم] ", text)
if self.remove_html_markup:
# remove html line breaks
text = re.sub("<br />", " ", text)
# remove html markup
text = re.sub("</?[^>]+>", " ", text)
if self.map_hindi_numbers_to_arabic:
text = text.translate(HINDI_TO_ARABIC_MAP)
# remove repeated characters >2
if self.remove_non_digit_repetition:
text = self._remove_non_digit_repetition(text)
# insert whitespace before and after all non Arabic digits or English Digits and Alphabet and the 2 brackets
if self.insert_white_spaces:
text = re.sub(
"([^0-9\u0621-\u063A\u0641-\u064A\u0660-\u0669a-zA-Z\[\]])",
r" \1 ",
text,
)
# insert whitespace between words and numbers or numbers and words
text = re.sub("(\d+)([\u0621-\u063A\u0641-\u064A\u0660-\u066C]+)",
r" \1 \2 ", text)
text = re.sub("([\u0621-\u063A\u0641-\u064A\u0660-\u066C]+)(\d+)",
r" \1 \2 ", text)
if self.replace_slash_with_dash:
text = text.replace("/", "-")
# remove unwanted characters
text = re.sub(self.REJECTED_CHARS_REGEX, " ", text)
# remove extra spaces
text = " ".join(text.replace("\uFE0F", "").split())
if (self.model_name == "bert-base-arabertv2"
or self.model_name == "bert-large-arabertv2"):
if self.keep_emojis:
new_text = []
for word in text.split():
if word in list(self.emoji.UNICODE_EMOJI["en"].keys()):
new_text.append(word)
else:
new_text.append(self.farasa_segmenter.segment(word))
text = " ".join(new_text)
else:
text = self.farasa_segmenter.segment(text)
return self._farasa_segment(text)
# ALl the other models dont require Farasa Segmentation
return text
u"الْعَرَيِيّةُ الفصحى",
u"غير مشكول",
"Taha",
]
word1 = u""
for word in word_list:
print word.encode('utf8'), '\t',
if araby.is_vocalized(word): print ' is vocalized',
## if araby.isArabicstring(word): print ' iisArabicstring',
## else:print ' invalid arabicstring',
if araby.is_vocalizedtext(word): print ' is vocalized text',
if araby.is_arabicword(word): print ' is valid word',
else: print "invalid arabic word",
print ' strip harakat', araby.strip_harakat(word).encode('utf8'),
print ' strip tashkeel', araby.strip_tashkeel(word).encode('utf8'),
print ' strip tatweel', araby.strip_tatweel(word).encode('utf8'),
print ' normalize ligature ', araby.normalize_ligature(word).encode(
'utf8'),
if araby.vocalizedlike(word, word1): print "vocalized_like",
print
word1 = word
if araby.vocalizedlike(u"العربية", u"العرَبية"): print "vocalized_like",
word = u"الْعَرَيِيّةُ"
word_list = [
u"الْعَرَيِيّةُ",
u"العربية",
u"الْعَرَيِيّةُ الفصحى",
u"غير مشكول",
"Taha",
]
word1 = u""
def strip_tatweel(text):
reduced = araby.strip_tatweel(text)
return reduced
def check_word(self, word, guessedtag = ""):
"""
Analyze one word morphologically as verbs
@param word: the input word.
@type word: unicode.
@return: list of dictionaries of analyzed words with tags.
@rtype: list.
"""
word = araby.strip_tatweel(word)
word_vocalised = word
word_nm = araby.strip_tashkeel(word)
# get analysed details from cache if used
if self.allow_cache_use and self.cache.isAlreadyChecked(word_nm):
#~ print (u"'%s'"%word).encode('utf8'), 'found'
resulted_data = self.cache.getChecked(word_nm)
else:
resulted_data = []
# if word is a pounctuation
resulted_data += self.check_word_as_pounct(word_nm)
# Done: if the word is a stop word we have some problems,
# the stop word can also be another normal word (verb or noun),
# we must consider it in future works
# if word is stopword allow stop words analysis
resulted_data += self.check_word_as_stopword(word_nm)
#if word is verb
# مشكلة بعض الكلمات المستبعدة تعتبر أفعلا أو اسماء
#~if self.tagger.has_verb_tag(guessedtag) or \
#~self.tagger.is_stopword_tag(guessedtag):
#~resulted_data += self.check_word_as_verb(word_nm)
resulted_data += self.check_word_as_verb(word_nm)
#print "is verb", rabti, len(resulted_data)
#if word is noun
#~if self.tagger.has_noun_tag(guessedtag) or \
#~self.tagger.is_stopword_tag(guessedtag):
#~resulted_data += self.check_word_as_noun(word_nm)
resulted_data += self.check_word_as_noun(word_nm)
if len(resulted_data) == 0:
print (u"1 _unknown %s-%s"%(word, word_nm)).encode('utf8')
#check the word as unkonwn
resulted_data += self.check_word_as_unknown(word_nm)
#check if the word is nomralized and solution are equivalent
resulted_data = check_normalized(word_vocalised, resulted_data)
#check if the word is shadda like
resulted_data = check_shadda(word_vocalised, resulted_data)
# add word frequency information in tags
resulted_data = self.add_word_frequency(resulted_data)
# add the stemmed words details into Cache
data_list_to_serialize = [w.__dict__ for w in resulted_data]
if self.allow_cache_use:
self.cache.addChecked(word_nm, data_list_to_serialize)
#check if the word is vocalized like results
if self.partial_vocalization_support:
resulted_data = check_partial_vocalized(word_vocalised, resulted_data)
if len(resulted_data) == 0:
resulted_data.append(wordcase.WordCase({
'word':word,
'affix': ('' , '', '', ''),
'stem':word,
'original':word,
'vocalized':word,
'semivocalized':word,
'tags':u'',
'type':'unknown',
'root':'',
'template':'',
'freq':self.wordfreq.get_freq(word, 'unknown'),
'syntax':'',
})
)
return resulted_data
# remove stop word
with open('clean_tweet.txt', 'r') as inFile, open('removeStopWordFile.txt', 'w') as outFile:
for line in inFile.readlines():
print(" ".join([word for word in line.split()
# if the word not in stop word write to file
if word not in stopwords.words('arabic', 'UTF-8')]), file=outFile)
inFile.close()
outFile.close()
with open('removeStopWordFile.txt', 'r') as f_input, open('RemoveT&T.txt', 'w') as f_output:
for line in f_input:
st = line
#مكه become مكــــــــــــــه
x = araby.strip_tatweel(st)
#ٍRemove َ ً ُِ ٌ
y = araby.strip_tashkeel(x)
# write to file
f_output.write(y)
f_input.close()
f_output.close()
# steaming the word
with open('RemoveT&T.txt', 'r') as f_input, open('steam.txt', 'w') as f_output:
for line in f_input.read().split("\n"):
sentence = u"" + line
for word in sentence.split(" "):
if word[-1:] == 'ة':
def check_word(self, word, guessedtag = ""):
"""
Analyze one word morphologically as verbs
@param word: the input word.
@type word: unicode.
@return: list of dictionaries of analyzed words with tags.
@rtype: list.
"""
word = araby.strip_tatweel(word)
word_vocalised = word
word_nm = araby.strip_tashkeel(word)
# get analysed details from cache if used
if self.allow_cache_use and self.cache.isAlreadyChecked(word_nm):
#~ print (u"'%s'"%word).encode('utf8'), 'found'
resulted_data = self.cache.getChecked(word_nm)
else:
resulted_data = []
# if word is a pounctuation
resulted_data += self.check_word_as_pounct(word_nm)
# Done: if the word is a stop word we have some problems,
# the stop word can also be another normal word (verb or noun),
# we must consider it in future works
# if word is stopword allow stop words analysis
resulted_data += self.check_word_as_stopword(word_nm)
#if word is verb
# مشكلة بعض الكلمات المستبعدة تعتبر أفعلا أو اسماء
#~if self.tagger.has_verb_tag(guessedtag) or \
#~self.tagger.is_stopword_tag(guessedtag):
#~resulted_data += self.check_word_as_verb(word_nm)
resulted_data += self.check_word_as_verb(word_nm)
#print "is verb", rabti, len(resulted_data)
#if word is noun
#~if self.tagger.has_noun_tag(guessedtag) or \
#~self.tagger.is_stopword_tag(guessedtag):
#~resulted_data += self.check_word_as_noun(word_nm)
resulted_data += self.check_word_as_noun(word_nm)
if len(resulted_data) == 0:
print (u"1 _unknown %s-%s"%(word, word_nm)).encode('utf8')
#check the word as unkonwn
resulted_data += self.check_word_as_unknown(word_nm)
#check if the word is nomralized and solution are equivalent
resulted_data = check_normalized(word_vocalised, resulted_data)
#check if the word is shadda like
resulted_data = check_shadda(word_vocalised, resulted_data)
# add word frequency information in tags
resulted_data = self.add_word_frequency(resulted_data)
# add the stemmed words details into Cache
data_list_to_serialize = [w.__dict__ for w in resulted_data]
if self.allow_cache_use:
self.cache.addChecked(word_nm, data_list_to_serialize)
#check if the word is vocalized like results
if self.partial_vocalization_support:
resulted_data = check_partial_vocalized(word_vocalised, resulted_data)
if len(resulted_data) == 0:
resulted_data.append(wordcase.WordCase({
'word':word,
'affix': ('' , '', '', ''),
'stem':'',
'original':word,
'vocalized':word,
'tags':u'',
'type':'unknown',
'root':'',
'template':'',
'freq':self.wordfreq.get_freq(word, 'unknown'),
'syntax':'',
})
)
return resulted_data
u"الْعَرَيِيّةُ الفصحى",
u"غير مشكول",
"Taha",
]
word1=u""
for word in word_list:
print word.encode('utf8'),'\t',
if araby.is_vocalized(word): print ' is vocalized',
## if araby.isArabicstring(word): print ' iisArabicstring',
## else:print ' invalid arabicstring',
if araby.is_vocalizedtext(word): print ' is vocalized text',
if araby.is_arabicword(word): print ' is valid word',
else: print "invalid arabic word",
print ' strip harakat', araby.strip_harakat(word).encode('utf8'),
print ' strip tashkeel', araby.strip_tashkeel(word).encode('utf8'),
print ' strip tatweel',araby.strip_tatweel(word).encode('utf8'),
print ' normalize ligature ', araby.normalize_ligature(word).encode('utf8'),
if araby.vocalizedlike(word, word1): print "vocalized_like",
print;
word1=word;
if araby.vocalizedlike(u"العربية",u"العرَبية"): print "vocalized_like",
word=u"الْعَرَيِيّةُ"
word_list=[
u"الْعَرَيِيّةُ",
u"العربية",
u"الْعَرَيِيّةُ الفصحى",
u"غير مشكول",
"Taha",
]
word1=u""
for word in word_list:
def strip_text(self, text):
return araby.strip_tatweel(araby.strip_tashkeel(text))
def run_diac(gomla, dialect):
sos = 'بدايةجملة' if dialect == 'ca' else 'بداية'
eos = 'نهايةجملة' if dialect == 'ca' else 'نهاية'
token_list_7 = LastNTokens(7, sos)
fname = randint(0, 100000)
with codecs.open(f'diacritizer/userdata/{dialect}/{fname}.fmt',
mode='w',
encoding='utf-8') as infile:
gomla = strip_tatweel(araby.normalize_ligature(gomla))
# gomla_list = araby.tokenize(gomla.replace('_', '-'), conditions=araby.is_arabicrange, morphs=araby.strip_tashkeel)
gomla_list = araby.tokenize(gomla.replace('_', '-'),
morphs=araby.strip_tashkeel)
# gomla_list = gomla.strip().split()
for token in gomla_list:
t = ' '.join(token)
token_list_7.add_tokens_list(t, 0)
infile.write(token_list_7.get_n_tokens() + '\n')
else:
token_list_7.add_tokens_list(eos, 0)
infile.write(token_list_7.get_n_tokens() + '\n')
token_list_7.add_tokens_list(eos, 0)
infile.write(token_list_7.get_n_tokens() + '\n')
token_list_7.add_tokens_list(eos, 0)
infile.write(token_list_7.get_n_tokens() + '\n')
token_list_7.add_tokens_list(eos, 0)
infile.write(token_list_7.get_n_tokens() + '\n')
token_list_7.add_tokens_list(eos, 0)
infile.write(token_list_7.get_n_tokens() + '\n')
token_list_7.add_tokens_list(eos, 0)
infile.write(token_list_7.get_n_tokens() + '\n')
if dialect == 'ca':
ca_runner.infer(
f"diacritizer/userdata/ca/{fname}.fmt",
predictions_file=f"diacritizer/userdata/ca/{fname}.rlt",
checkpoint_path=None,
log_time=False)
elif dialect == 'msa':
msa_runner.infer(
f"diacritizer/userdata/msa/{fname}.fmt",
predictions_file=f"diacritizer/userdata/msa/{fname}.rlt",
checkpoint_path=None,
log_time=False)
elif dialect == 'tun':
tn_runner.infer(
f"diacritizer/userdata/tun/{fname}.fmt",
predictions_file=f"diacritizer/userdata/tun/{fname}.rlt",
checkpoint_path=None,
log_time=False)
elif dialect == 'mor':
ma_runner.infer(
f"diacritizer/userdata/mor/{fname}.fmt",
predictions_file=f"diacritizer/userdata/mor/{fname}.rlt",
checkpoint_path=None,
log_time=False)
with codecs.open(f'diacritizer/userdata/{dialect}/{fname}.rlt',
mode='r',
encoding='utf-8') as outfile:
diacritized_tokens = list()
counters = defaultdict(Counter)
for i, line in enumerate(outfile):
dtokens = line.strip().split(' _ ')
# print(len(dtokens), dtokens)
for j, _ in enumerate(dtokens):
tk = dtokens[j - 1 - i % 7]
if tk not in [eos, sos]:
counters[j].update([tk])
if sum(counters[j].values()) >= 7:
diacritized_tokens.append(
counters[j].most_common(1)[0][0].replace(' ', ''))
counters[j].clear()
else:
return ' '.join(diacritized_tokens)
def preprocess(self, text):
"""
Preprocess takes an input text line an applies the same preprocessing used in AraBERT
pretraining
Args:
text (:obj:`str`): inout text string
Returns:
string: A preprocessed string depending on which model was selected
"""
if self.model_name == "bert-base-arabert":
return self._old_preprocess(
text,
do_farasa_tokenization=True,
)
if self.model_name == "bert-base-arabertv01":
return self._old_preprocess(text, do_farasa_tokenization=False)
text = str(text)
text = html.unescape(text)
if self.strip_tashkeel:
text = araby.strip_tashkeel(text)
if self.strip_tatweel:
text = araby.strip_tatweel(text)
if self.replace_urls_emails_mentions:
# replace all possible URLs
for reg in url_regexes:
text = re.sub(reg, " [رابط] ", text)
# REplace Emails with [بريد]
for reg in email_regexes:
text = re.sub(reg, " [بريد] ", text)
# replace mentions with [مستخدم]
text = re.sub(user_mention_regex, " [مستخدم] ", text)
if self.remove_html_markup:
# remove html line breaks
text = re.sub("<br />", " ", text)
# remove html markup
text = re.sub("</?[^>]+>", " ", text)
# remove repeated characters >2
if self.remove_elongation:
text = self._remove_elongation(text)
# insert whitespace before and after all non Arabic digits or English Digits and Alphabet and the 2 brackets
if self.insert_white_spaces:
text = re.sub(
"([^0-9\u0621-\u063A\u0641-\u064A\u0660-\u0669a-zA-Z\[\]])",
r" \1 ",
text,
)
# insert whitespace between words and numbers or numbers and words
text = re.sub("(\d+)([\u0621-\u063A\u0641-\u064A\u0660-\u066C]+)",
r" \1 \2 ", text)
text = re.sub("([\u0621-\u063A\u0641-\u064A\u0660-\u066C]+)(\d+)",
r" \1 \2 ", text)
# remove unwanted characters
if self.keep_emojis:
emoji_regex = "".join(list(self.emoji.UNICODE_EMOJI["en"].keys()))
rejected_chars_regex2 = "[^%s%s]" % (chars_regex, emoji_regex)
text = re.sub(rejected_chars_regex2, " ", text)
else:
text = re.sub(rejected_chars_regex, " ", text)
# remove extra spaces
text = " ".join(text.replace("\uFE0F", "").split())
if (self.model_name == "bert-base-arabertv2"
or self.model_name == "bert-large-arabertv2"):
if self.keep_emojis:
new_text = []
for word in text.split():
if word in list(self.emoji.UNICODE_EMOJI["en"].keys()):
new_text.append(word)
else:
new_text.append(self.farasa_segmenter.segment(word))
text = " ".join(new_text)
else:
text = self.farasa_segmenter.segment(text)
return self._farasa_segment(text)
# ALl the other models dont require Farasa Segmentation
return text
def from_url(url):
""" Initialize a Question (written or oral) from its public URL
e.g.: https://www.chambredesrepresentants.ma/ar/%D9%85%D8%B1%D8%A7%D9%82%D8%A8%D8%A9-%D8%A7%D9%84%D8%B9%D9%85%D9%84-%D8%A7%D9%84%D8%AD%D9%83%D9%88%D9%85%D9%8A/%D8%A7%D9%84%D8%A3%D8%B3%D9%80%D8%A6%D9%84%D8%A9-%D8%A7%D9%84%D9%83%D8%AA%D8%A7%D8%A8%D9%8A%D8%A9/%D8%A7%D9%86%D8%AA%D8%B4%D8%A7%D8%B1-%D8%A7%D9%84%D8%AD%D8%B4%D8%B1%D8%A9-%D8%A7%D9%84%D9%82%D8%B1%D9%85%D8%B2%D9%8A%D8%A9-%D8%A8%D9%86%D8%A8%D8%A7%D8%AA-%D8%A7%D9%84%D8%B5%D8%A8%D8%A7%D8%B1
Note: only the arabic version is currently supported."""
r = requests.get(url)
s = BeautifulSoup(r.text, 'html.parser')
try:
# Note : strip_tatweel below is mandatory, it took me several hours of scratching
title = araby.strip_tatweel(
s.find_all(
'h1', class_='section-title title-global lang_ar')[0].text)
title.lstrip().rstrip()
qtype = 'undefined'
if arabic_string_eq(title, "الأسئلة الكتابية"):
qtype = 'written'
elif arabic_string_eq(title, "الأسئلة الشفوية"):
qtype = 'oral'
spans = s.find_all(class_='q-b1-1')[0].find_all('span')
status = 'unknown'
if 'q-st-red' in spans[4]['class']:
status = 'unanswered'
elif 'q-st-green' in spans[4]['class']:
status = 'answered'
if status == 'unanswered':
content = s.find_all(class_='q-block1-na')[0]
elif status == 'answered':
content = s.find_all(class_='q-block1')[0]
else:
raise ValueError("Malformed HTML")
qb11 = content.find_all(class_='q-b1-1')[0].find_all('span')
# XXX How do we handle RE faulty cases ?
p = re.compile('رقم السؤال : ([0-9]+)')
res = p.match(qb11[0].text.lstrip().rstrip())
id = int(res.group(1))
p = re.compile('الموضوع : (.*)')
res = p.match(qb11[1].text.lstrip().rstrip())
topic = res.group(1)
answer_date_raw = ''
if arabic_string_eq(qb11[2].text.split(':')[0].lstrip().rstrip(),
'تاريخ الجواب'):
answer_date_raw = qb11[3].text.lstrip().rstrip()
answer_date = format_raw_date_to_isoformat(answer_date_raw)
qb12 = content.find_all(class_='q-b1-2 row')[0]
team = qb12.find_all(class_='col-md-5')[0].find_all('a')[0].text
questioners = []
for q in qb12.find_all(class_='col-md-7')[0].find_all(
class_='q-b1-2-s-item'):
questioners.append(
q.find_all(
class_='q-name')[0].find('a').text.lstrip().rstrip())
qb13 = content.find_all(class_='q-b1-3')[0]
offset = 0
designated_ministry = ''
# XXX : are there cases where there are multiple ministries involved?
if arabic_string_eq(
qb13.find_all('div')[0].text.split(':')
[0].lstrip().rstrip(), 'الوزارة المختصة'):
designated_ministry = qb13.find_all('div')[0].text.split(
':')[1].replace('\n', '').lstrip().rstrip()
offset = 1
question_date_raw = ''
question_text = ''
if qtype == "written":
if arabic_string_eq(
qb13.find_all('div')[offset].text.split(':')
[0].lstrip().rstrip(), 'تاريخ السؤال'):
question_date_raw = qb13.find_all(
'div')[offset].text.split(':')[1].replace(
'\n', '').lstrip().rstrip()
if arabic_string_eq(
qb13.find_all('div')[offset + 1].text.split(':')
[0].lstrip().rstrip(), 'السؤال'):
question_text = qb13.find_all('div')[offset + 1].find(
'p').text.lstrip().rstrip()
elif qtype == "oral":
# TODO: check if there are cases where the date is given
if arabic_string_eq(
qb13.find_all('div')[offset].text.split(':')
[0].lstrip().rstrip(), 'السؤال'):
question_text = qb13.find_all('div')[offset].find(
'p').text.lstrip().rstrip()
question_date = format_raw_date_to_isoformat(question_date_raw)
answer_doc = ''
answer_content = s.find_all(class_='q-block2')
if answer_content != []:
answer_doc = answer_content[0].find_all('a',
href=True)[0]['href']
q = Question(id, url, qtype, topic, question_date, question_text,
questioners, status, answer_date, answer_doc,
designated_ministry)
return q
except Exception as e:
print('There was an exception: %s' % e)
return None
u"الْعَرَيِيّةُ",
u"العربية",
u"الْعَرَيِيّةُ الفصحى",
u"غير مشكول",
"Taha",
]
word1 = u""
for word in word_list:
print(word, '\t', end=" ")
if araby.is_vocalized(word): print(' is vocalized', end=" ")
if araby.is_vocalizedtext(word): print(' is vocalized text', end=" ")
if araby.is_arabicword(word): print(' is valid word', end=" ")
else: print("invalid arabic word", end=" ")
print(' strip harakat', araby.strip_harakat(word), end=" ")
print(' strip tashkeel', araby.strip_tashkeel(word), end=" ")
print(' strip tatweel', araby.strip_tatweel(word), end=" ")
print(' normalize ligature ', araby.normalize_ligature(word), end=" ")
if araby.vocalizedlike(word, word1): print("vocalized_like", end=" ")
print()
word1 = word
if araby.vocalizedlike(u"العربية", u"العرَبية"):
print("vocalized_like", end=" ")
word = u"الْعَرَيِيّةُ"
word_list = [
u"الْعَرَيِيّةُ",
u"العربية",
u"الْعَرَيِيّةُ الفصحى",
u"غير مشكول",
"Taha",
]
word1 = u""
def strip_text(text): # removes arabic vowels and stylistic spacing
return araby.strip_tatweel(araby.strip_tashkeel(text))
