문장에서 파이썬 분할 텍스트

문장에서 파이썬 분할 텍스트

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텍스트 파일이 있습니다. 문장 목록이 필요합니다.

어떻게 구현할 수 있습니까? 약어에 점이 사용되는 등 미묘한 부분이 많이 있습니다.

내 오래된 정규식이 잘못 작동합니다.

re.compile('(\. |^|!|\?)([A-Z][^;↑\.<>@\^&/\[\]]*(\.|!|\?) )',re.M)

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Natural Language Toolkit ( nltk.org )에는 필요한 것이 있습니다. 이 그룹 게시물 은 다음과 같이 표시됩니다.

import nltk.data

tokenizer = nltk.data.load('tokenizers/punkt/english.pickle')
fp = open("test.txt")
data = fp.read()
print '\n-----\n'.join(tokenizer.tokenize(data))

(나는 그것을 시도하지 않았습니다!)

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이 기능은 약 0.1 초 만에 Huckleberry Finn의 전체 텍스트를 문장으로 분할 할 수 있으며 " Mr. John Johnson Jr.는 미국에서 태어 났지만 박사 학위를 취득했습니다. D.는 엔지니어로 Nike Inc.에 합류하기 전에 이스라엘에서 근무했으며 craigslist.org에서 비즈니스 분석가로 일했습니다. "

# -*- coding: utf-8 -*-
import re
alphabets= "([A-Za-z])"
prefixes = "(Mr|St|Mrs|Ms|Dr)[.]"
suffixes = "(Inc|Ltd|Jr|Sr|Co)"
starters = "(Mr|Mrs|Ms|Dr|He\s|She\s|It\s|They\s|Their\s|Our\s|We\s|But\s|However\s|That\s|This\s|Wherever)"
acronyms = "([A-Z][.][A-Z][.](?:[A-Z][.])?)"
websites = "[.](com|net|org|io|gov)"

def split_into_sentences(text):
    text = " " + text + "  "
    text = text.replace("\n"," ")
    text = re.sub(prefixes,"\\1<prd>",text)
    text = re.sub(websites,"<prd>\\1",text)
    if "Ph.D" in text: text = text.replace("Ph.D.","Ph<prd>D<prd>")
    text = re.sub("\s" + alphabets + "[.] "," \\1<prd> ",text)
    text = re.sub(acronyms+" "+starters,"\\1<stop> \\2",text)
    text = re.sub(alphabets + "[.]" + alphabets + "[.]" + alphabets + "[.]","\\1<prd>\\2<prd>\\3<prd>",text)
    text = re.sub(alphabets + "[.]" + alphabets + "[.]","\\1<prd>\\2<prd>",text)
    text = re.sub(" "+suffixes+"[.] "+starters," \\1<stop> \\2",text)
    text = re.sub(" "+suffixes+"[.]"," \\1<prd>",text)
    text = re.sub(" " + alphabets + "[.]"," \\1<prd>",text)
    if "”" in text: text = text.replace(".”","”.")
    if "\"" in text: text = text.replace(".\"","\".")
    if "!" in text: text = text.replace("!\"","\"!")
    if "?" in text: text = text.replace("?\"","\"?")
    text = text.replace(".",".<stop>")
    text = text.replace("?","?<stop>")
    text = text.replace("!","!<stop>")
    text = text.replace("<prd>",".")
    sentences = text.split("<stop>")
    sentences = sentences[:-1]
    sentences = [s.strip() for s in sentences]
    return sentences

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Instead of using regex for spliting the text into sentences, you can also use nltk library.

>>> from nltk import tokenize
>>> p = "Good morning Dr. Adams. The patient is waiting for you in room number 3."

>>> tokenize.sent_tokenize(p)
['Good morning Dr. Adams.', 'The patient is waiting for you in room number 3.']

ref: https://stackoverflow.com/a/9474645/2877052

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Here is a middle of the road approach that doesn't rely on any external libraries. I use list comprehension to exclude overlaps between abbreviations and terminators as well as to exclude overlaps between variations on terminations, for example: '.' vs. '."'

abbreviations = {'dr.': 'doctor', 'mr.': 'mister', 'bro.': 'brother', 'bro': 'brother', 'mrs.': 'mistress', 'ms.': 'miss', 'jr.': 'junior', 'sr.': 'senior',
                 'i.e.': 'for example', 'e.g.': 'for example', 'vs.': 'versus'}
terminators = ['.', '!', '?']
wrappers = ['"', "'", ')', ']', '}']


def find_sentences(paragraph):
   end = True
   sentences = []
   while end > -1:
       end = find_sentence_end(paragraph)
       if end > -1:
           sentences.append(paragraph[end:].strip())
           paragraph = paragraph[:end]
   sentences.append(paragraph)
   sentences.reverse()
   return sentences


def find_sentence_end(paragraph):
    [possible_endings, contraction_locations] = [[], []]
    contractions = abbreviations.keys()
    sentence_terminators = terminators + [terminator + wrapper for wrapper in wrappers for terminator in terminators]
    for sentence_terminator in sentence_terminators:
        t_indices = list(find_all(paragraph, sentence_terminator))
        possible_endings.extend(([] if not len(t_indices) else [[i, len(sentence_terminator)] for i in t_indices]))
    for contraction in contractions:
        c_indices = list(find_all(paragraph, contraction))
        contraction_locations.extend(([] if not len(c_indices) else [i + len(contraction) for i in c_indices]))
    possible_endings = [pe for pe in possible_endings if pe[0] + pe[1] not in contraction_locations]
    if len(paragraph) in [pe[0] + pe[1] for pe in possible_endings]:
        max_end_start = max([pe[0] for pe in possible_endings])
        possible_endings = [pe for pe in possible_endings if pe[0] != max_end_start]
    possible_endings = [pe[0] + pe[1] for pe in possible_endings if sum(pe) > len(paragraph) or (sum(pe) < len(paragraph) and paragraph[sum(pe)] == ' ')]
    end = (-1 if not len(possible_endings) else max(possible_endings))
    return end


def find_all(a_str, sub):
    start = 0
    while True:
        start = a_str.find(sub, start)
        if start == -1:
            return
        yield start
        start += len(sub)

I used Karl's find_all function from this entry: Find all occurrences of a substring in Python

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You can try using Spacy instead of regex. I use it and it does the job.

import spacy
nlp = spacy.load('en')

text = '''Your text here'''
tokens = nlp(text)

for sent in tokens.sents:
    print(sent.string.strip())

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For simple cases (where sentences are terminated normally), this should work:

import re
text = ''.join(open('somefile.txt').readlines())
sentences = re.split(r' *[\.\?!][\'"\)\]]* *', text)

The regex is *\. +, which matches a period surrounded by 0 or more spaces to the left and 1 or more to the right (to prevent something like the period in re.split being counted as a change in sentence).

Obviously, not the most robust solution, but it'll do fine in most cases. The only case this won't cover is abbreviations (perhaps run through the list of sentences and check that each string in sentences starts with a capital letter?)

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@Artyom,

Hi! You could make a new tokenizer for Russian (and some other languages) using this function:

def russianTokenizer(text):
    result = text
    result = result.replace('.', ' . ')
    result = result.replace(' .  .  . ', ' ... ')
    result = result.replace(',', ' , ')
    result = result.replace(':', ' : ')
    result = result.replace(';', ' ; ')
    result = result.replace('!', ' ! ')
    result = result.replace('?', ' ? ')
    result = result.replace('\"', ' \" ')
    result = result.replace('\'', ' \' ')
    result = result.replace('(', ' ( ')
    result = result.replace(')', ' ) ') 
    result = result.replace('  ', ' ')
    result = result.replace('  ', ' ')
    result = result.replace('  ', ' ')
    result = result.replace('  ', ' ')
    result = result.strip()
    result = result.split(' ')
    return result

and then call it in this way:

text = 'вы выполняете поиск, используя Google SSL;'
tokens = russianTokenizer(text)

Good luck, Marilena.

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No doubt that NLTK is the most suitable for the purpose. But getting started with NLTK is quite painful (But once you install it - you just reap the rewards)

So here is simple re based code available at http://pythonicprose.blogspot.com/2009/09/python-split-paragraph-into-sentences.html

# split up a paragraph into sentences
# using regular expressions


def splitParagraphIntoSentences(paragraph):
    ''' break a paragraph into sentences
        and return a list '''
    import re
    # to split by multile characters

    #   regular expressions are easiest (and fastest)
    sentenceEnders = re.compile('[.!?]')
    sentenceList = sentenceEnders.split(paragraph)
    return sentenceList


if __name__ == '__main__':
    p = """This is a sentence.  This is an excited sentence! And do you think this is a question?"""

    sentences = splitParagraphIntoSentences(p)
    for s in sentences:
        print s.strip()

#output:
#   This is a sentence
#   This is an excited sentence

#   And do you think this is a question 

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You can also use sentence tokenization function in NLTK:

from nltk.tokenize import sent_tokenize
sentence = "As the most quoted English writer Shakespeare has more than his share of famous quotes.  Some Shakespare famous quotes are known for their beauty, some for their everyday truths and some for their wisdom. We often talk about Shakespeare’s quotes as things the wise Bard is saying to us but, we should remember that some of his wisest words are spoken by his biggest fools. For example, both ‘neither a borrower nor a lender be,’ and ‘to thine own self be true’ are from the foolish, garrulous and quite disreputable Polonius in Hamlet."

sent_tokenize(sentence)

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I had to read subtitles files and split them into sentences. After pre-processing (like removing time information etc in the .srt files), the variable fullFile contained the full text of the subtitle file. The below crude way neatly split them into sentences. Probably I was lucky that the sentences always ended (correctly) with a space. Try this first and if it has any exceptions, add more checks and balances.

# Very approximate way to split the text into sentences - Break after ? . and !
fullFile = re.sub("(\!|\?|\.) ","\\1<BRK>",fullFile)
sentences = fullFile.split("<BRK>");
sentFile = open("./sentences.out", "w+");
for line in sentences:
    sentFile.write (line);
    sentFile.write ("\n");
sentFile.close;

Oh! well. I now realize that since my content was Spanish, I did not have the issues of dealing with "Mr. Smith" etc. Still, if someone wants a quick and dirty parser...

참고URL : https://stackoverflow.com/questions/4576077/python-split-text-on-sentences