def __init__(
self,
custom_dict: List[Tuple[str, int]] = None,
min_freq: int = 2,
min_len: int = 2,
max_len: int = 40,
dict_filter: Callable[[str], bool] = _is_thai_and_not_num,
):
"""
Initialize Peter Norvig's spell checker object
:param str custom_dict: A list of tuple (word, frequency) to create a spelling dictionary. Default is from Thai National Corpus (around 40,000 words).
:param int min_freq: Minimum frequency of a word to keep (default = 2)
:param int min_len: Minimum length (in characters) of a word to keep (default = 2)
:param int max_len: Maximum length (in characters) of a word to keep (default = 40)
:param func dict_filter: A function to filter the dictionary. Default filter removes any word with number or non-Thai characters. If no filter is required, use None.
"""
if not custom_dict: # default, use Thai National Corpus
custom_dict = tnc.word_freqs()
if not dict_filter:
dict_filter = _no_filter
# filter word list
custom_dict = [
word_freq for word_freq in custom_dict
if _keep(word_freq, min_freq, min_len, max_len, dict_filter)
]
self.__WORDS = Counter(dict(custom_dict))
self.__WORDS_TOTAL = sum(self.__WORDS.values())
if self.__WORDS_TOTAL < 1:
self.__WORDS_TOTAL = 0
def __init__(
self,
custom_dict: List[Tuple[str, int]] = None,
min_freq: int = 2,
min_len: int = 2,
max_len: int = 40,
dict_filter: Callable[[str], bool] = _is_thai_and_not_num,
):
"""
Initialize Peter Norvig's spell checker object
:param str custom_dict: A list of tuple (word, frequency) to create a spelling dictionary. Default is from Thai National Corpus (around 40,000 words).
:param int min_freq: Minimum frequency of a word to keep (default = 2)
:param int min_len: Minimum length (in characters) of a word to keep (default = 2)
:param int max_len: Maximum length (in characters) of a word to keep (default = 40)
:param func dict_filter: A function to filter the dictionary. Default filter removes any word with number or non-Thai characters. If no filter is required, use None.
"""
if not custom_dict: # default, use Thai National Corpus
custom_dict = tnc.word_freqs()
if not dict_filter:
dict_filter = _no_filter
# filter word list
custom_dict = [
word_freq
for word_freq in custom_dict
if _keep(word_freq, min_freq, min_len, max_len, dict_filter)
]
self.__WORDS = Counter(dict(custom_dict))
self.__WORDS_TOTAL = sum(self.__WORDS.values())
if self.__WORDS_TOTAL < 1:
self.__WORDS_TOTAL = 0
def __init__(
self,
custom_dict: Union[Dict[str, int], Iterable[str],
Iterable[Tuple[str, int]]] = None,
min_freq: int = 2,
min_len: int = 2,
max_len: int = 40,
dict_filter: Optional[Callable[[str], bool]] = _is_thai_and_not_num,
):
"""
Initializes Peter Norvig's spell checker object.
Spelling dictionary can be customized.
By default, spelling dictionary is from
`Thai National Corpus <http://www.arts.chula.ac.th/ling/tnc/>`_
Basically, Norvig's spell checker will choose the most likely
spelling correction give a word by searching for candidate
corrected words based on edit distance.
Then, it selects the candidate with
the highest word occurrence probability.
:param str custom_dict: A custom spelling dictionary. This can be:
(1) a dictionary (`dict`), with words (`str`)
as keys and frequencies (`int`) as values;
(2) an iterable (list, tuple, or set) of word
(`str`) and frequency (`int`) tuples:
`(str, int)`; or
(3) an iterable of just words (`str`), without
frequencies -- in this case `1` will be
assigned to every words.
Default is from Thai National Corpus (around
40,000 words).
:param int min_freq: Minimum frequency of a word to keep (default = 2)
:param int min_len: Minimum length (in characters) of a word to keep
(default = 2)
:param int max_len: Maximum length (in characters) of a word to keep
(default = 40)
:param func dict_filter: A function to filter the dictionary.
Default filter removes any word
with number or non-Thai characters.
If no filter is required, use None.
"""
if not custom_dict: # default, use Thai National Corpus
custom_dict = tnc.word_freqs()
if not dict_filter:
dict_filter = _no_filter
custom_dict = _convert_custom_dict(custom_dict, min_freq, min_len,
max_len, dict_filter)
self.__WORDS = Counter(dict(custom_dict))
self.__WORDS += Counter() # remove zero and negative counts
self.__WORDS_TOTAL = sum(self.__WORDS.values())
def __init__(
self,
custom_dict: List[Tuple[str, int]] = None,
min_freq: int = 2,
min_len: int = 2,
max_len: int = 40,
dict_filter: Optional[Callable[[str], bool]] = _is_thai_and_not_num,
):
"""
Initializes Peter Norvig's spell checker object.
Spelling dictionary can be customized.
By default, spelling dictionary is from
`Thai National Corpus <http://www.arts.chula.ac.th/ling/tnc/>`_
Basically, Norvig's spell checker will choose the most likely
spelling correction give a word by searching for candidate
corrected words based on edit distance.
Then, it selects the candidate with
the highest word occurrence probability.
:param str custom_dict: A list of tuple (word, frequency) to create
a spelling dictionary. Default is from
Thai National Corpus (around 40,000 words).
:param int min_freq: Minimum frequency of a word to keep (default = 2)
:param int min_len: Minimum length (in characters) of a word to keep
(default = 2)
:param int max_len: Maximum length (in characters) of a word to keep
(default = 40)
:param func dict_filter: A function to filter the dictionary.
Default filter removes any word
with number or non-Thai characters.
If no filter is required, use None.
"""
if not custom_dict: # default, use Thai National Corpus
custom_dict = tnc.word_freqs()
if not dict_filter:
dict_filter = _no_filter
# filter word list
custom_dict = [
word_freq for word_freq in custom_dict
if _keep(word_freq, min_freq, min_len, max_len, dict_filter)
]
self.__WORDS = Counter(dict(custom_dict))
self.__WORDS_TOTAL = sum(self.__WORDS.values())
if self.__WORDS_TOTAL < 1:
self.__WORDS_TOTAL = 0
def Tokenize_word(self,text):
######## Thai word segment ######## ver1
'''sent = text[0].replace("'","")
word = word_tokenize(sent, engine='deepcut') # use this method
wword = [x.replace('.',' ').replace('=',' ').replace('-',' ').replace("("," ").replace(")"," ").replace("/"," ").replace('สำหรับ',' ').replace('%',' ').strip(' ') for x in word]
words =[]
for w in wword:
if w not in common.thai_stopwords():
words = [str for str in words if str]
words.append(w)
return words'''
######## Thai word segment ######## ver2 -> stopwords, type of words, check spell(Eng & Thai)
sent = text[0].replace("'","")
word = word_tokenize(sent, engine='deepcut') # use this method
#wword = [x.replace('=',' ').replace('-',' ').replace("("," ").replace(")"," ").replace("/"," ").strip(' ') for x in word]
th_no_stopwords =[]
all_no_stopwords =[]
th_correct_words =[]
eng_correct_words =[]
mix_correct_words =[]
mix1_correct_words =[]
all_correct_words =[]
all_correct_words_final =[]
check_thai_list = []
#for tw in wword:
for tw in word:
if tw not in common.thai_stopwords():
th_no_stopwords = [str for str in th_no_stopwords if str]
th_no_stopwords.append(tw)
#print("th_no_stopwords = ", th_no_stopwords)
for ew in th_no_stopwords:
if ew not in stopwords.words('english'):
all_no_stopwords = [str for str in all_no_stopwords if str]
all_no_stopwords.append(ew)
#print("all_no_stopwords = ", all_no_stopwords)
for c in all_no_stopwords:
thai = isthai(c)
number = c.isnumeric()
if not thai:
no_num = c.isalpha()
match1 = re.findall('\D', c) #Return ถ้าไม่พบตัวเลข 0-9 ใน string
if no_num:
spell = SpellChecker()
eng_correct = spell.correction(c) #pn
eng_correct_words.append(eng_correct)
#print("eng = ", eng_correct)
elif match1:
mix = c
mix_correct_words.append(mix)
#print("mix = ", mix)
else:
num = c #No return
#print("num = ", num)
elif thai:
checker = NorvigSpellChecker(custom_dict=tnc.word_freqs()) #pn
th_correct = checker.correct(c)
th_correct_words.append(th_correct)
#print("thai = ", th_correct)
all_correct_words = th_correct_words + eng_correct_words + mix_correct_words
all_correct_words = [x.replace('น.','').replace(':',' ').replace('=',' ').replace('–',' ').replace("("," ").replace(")"," ").replace("/"," ").strip(" ") for x in all_correct_words]
all_correct_words_final = list(filter(None, all_correct_words))
#print("words = ", all_correct_words_final)
return all_correct_words_final
######## Eng word segment ########
'''word = text[0]
def test_tnc(self):
self.assertIsNotNone(tnc.word_freqs())
self.assertIsNotNone(tnc.unigram_word_freqs())
self.assertIsNotNone(tnc.bigram_word_freqs())
self.assertIsNotNone(tnc.trigram_word_freqs())
def test_tnc(self):
self.assertIsNotNone(tnc.word_freqs())
def test_tnc(self):
self.assertIsNotNone(tnc.word_freqs())
self.assertIsNotNone(tnc.word_freq("นก"))
