I have a text file. I need to get a list of sentences.
How can this be implemented? There are a lot of subtleties, such as a dot being used in abbreviations.
My old regular expression works badly:
re.compile('(\. |^|!|\?)([A-Z][^;↑\.<>@\^&/\[\]]*(\.|!|\?) )',re.M)
The Natural Language Toolkit (nltk.org) has what you need. This group posting indicates this does it:
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))
(I haven't tried it!)
nltk .tokenize.punkt.PunktSentenceTokenizer. –
Electro nltk.download() first and download models -> punkt –
Ensheathe import nltk then nltk.sent_tokenize(string) –
Cinderella 'This fails on cases with ending quotation marks. If we have a sentence that ends like "this." This is another sentence.' and my output is ['This fails on cases with ending quotation marks.', 'If we have a sentence that ends like "this."', 'This is another sentence.'] Seems correct for me. –
Brickbat 4. Building, sculpting, moving, and mending things in hard to reach places and at small scales (e.g. dig tunnels, deliver adhesives to cracks). –
Volitant This function can split the entire text of Huckleberry Finn into sentences in about 0.1 seconds and handles many of the more painful edge cases that make sentence parsing non-trivial e.g. "Mr. John Johnson Jr. was born in the U.S.A but earned his Ph.D. in Israel before joining Nike Inc. as an engineer. He also worked at craigslist.org as a business analyst."
# -*- 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|Prof|Capt|Cpt|Lt|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|edu|me)"
digits = "([0-9])"
multiple_dots = r'\.{2,}'
def split_into_sentences(text: str) -> list[str]:
"""
Split the text into sentences.
If the text contains substrings "<prd>" or "<stop>", they would lead
to incorrect splitting because they are used as markers for splitting.
:param text: text to be split into sentences
:type text: str
:return: list of sentences
:rtype: list[str]
"""
text = " " + text + " "
text = text.replace("\n"," ")
text = re.sub(prefixes,"\\1<prd>",text)
text = re.sub(websites,"<prd>\\1",text)
text = re.sub(digits + "[.]" + digits,"\\1<prd>\\2",text)
text = re.sub(multiple_dots, lambda match: "<prd>" * len(match.group(0)) + "<stop>", 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 = [s.strip() for s in sentences]
if sentences and not sentences[-1]: sentences = sentences[:-1]
return sentences
nltk:>>> from nltk.tokenize import sent_tokenize
Example 1: split_into_sentences is better here (because it explicitly covers a lot of cases):
>>> text = 'Some sentence. Mr. Holmes...This is a new sentence!And This is another one.. Hi '
>>> split_into_sentences(text)
['Some sentence.',
'Mr. Holmes...',
'This is a new sentence!',
'And This is another one..',
'Hi']
>>> sent_tokenize(text)
['Some sentence.',
'Mr.',
'Holmes...This is a new sentence!And This is another one.. Hi']
Example 2: nltk.tokenize.sent_tokenize is better here (because it uses an ML model):
>>> text = 'The U.S. Drug Enforcement Administration (DEA) says hello. And have a nice day.'
>>> split_into_sentences(text)
['The U.S.',
'Drug Enforcement Administration (DEA) says hello.',
'And have a nice day.']
>>> sent_tokenize(text)
['The U.S. Drug Enforcement Administration (DEA) says hello.',
'And have a nice day.']
prefixes = "(Mr|St|Mrs|Ms|Dr|Prof|Capt|Cpt|Lt|Mt)[.]", websites = "[.](com|net|org|io|gov|me|edu)", and if "..." in text: text = text.replace("...","<prd><prd><prd>") –
Grimona text = re.sub(" (\d+)[.](\d+) "," \\1<prd>\\2 ",text) –
Albemarle Thank you for contacting back. Request you to please help us with the transaction ID for $<***>.92 ? - Charlie. –
Prosthodontics "The U.S. Drug Enforcement Administration (DEA) says hello. And have a nice day." is splitting after "U.S." for some reason. –
Plotinus 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://mcmap.net/q/137284/-how-to-break-up-a-paragraph-by-sentences-in-python
for sentence in tokenize.sent_tokenize(text): print(sentence) –
Filide 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())
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
... and ?!. –
Embosser I love spaCy to death, but I recently discovered two new approaches for sentence tokenization. One is BlingFire from Microsoft (incredibly fast), and the other is PySBD from AI2 (supremely accurate).
text = ...
from blingfire import text_to_sentences
sents = text_to_sentences(text).split('\n')
from pysbd import Segmenter
segmenter = Segmenter(language='en', clean=False)
sents = segmenter.segment(text)
I separated 20k sentences using five different methods. Here are the elapsed times on an AMD Threadripper Linux machine:
UPDATE: I tried using BlingFire on all-lowercase text, and it failed miserably. I'm going to use PySBD in my projects for the time being.
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)
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?)
SyntaxError: EOL while scanning string literal, pointing to the closing parenthesis (after text). Also, the regex you reference in your text does not exist in your code sample. –
Dunite r' *[\.\?!][\'"\)\]]* +' –
Cyanate Using spacy:
import spacy
nlp = spacy.load('en_core_web_sm')
text = "How are you today? I hope you have a great day"
tokens = nlp(text)
for sent in tokens.sents:
print(sent.string.strip())
Might as well throw this in, since this is the first post that showed up for sentence split by n sentences.
This works with a variable split length, which indicates the sentences that get joined together in the end.
import nltk
//nltk.download('punkt')
from more_itertools import windowed
split_length = 3 // 3 sentences for example
elements = nltk.tokenize.sent_tokenize(text)
segments = windowed(elements, n=split_length, step=split_length)
text_splits = []
for seg in segments:
txt = " ".join([t for t in seg if t])
if len(txt) > 0:
text_splits.append(txt)
If NLTK's sent_tokenize is not a thing (e.g. needs a lot of GPU RAM on long text) and regex doesn't work properly across languages, sentence splitter might be try worth.
Using Stanza a natural language processing library that works for many human languages.
import stanza
stanza.download('en')
nlp = stanza.Pipeline(lang='en', processors='tokenize')
doc = nlp(t_en)
for sentence in doc.sentences:
print(sentence.text)
Also, be wary of additional top level domains that aren't included in some of the answers above.
For example .info, .biz, .ru, .online will throw some sentence parsers but aren't included above.
Here's some info on frequency of top level domains: https://www.westhost.com/blog/the-most-popular-top-level-domains-in-2017/
That could be addressed by editing the code above to read:
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|ai|edu|co.uk|ru|info|biz|online)"
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)
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
i hope this will help you on latin,chinese,arabic text
import re
punctuation = re.compile(r"([^\d+])(\.|!|\?|;|\n|。|！|？|；|…| |!|؟|؛)+")
lines = []
with open('myData.txt','r',encoding="utf-8") as myFile:
lines = punctuation.sub(r"\1\2<pad>", myFile.read())
lines = [line.strip() for line in lines.split("<pad>") if line.strip()]
Was working on similar task and came across this query, by following few links and working on few exercises for nltk the below code worked for me like magic.
from nltk.tokenize import sent_tokenize
text = "Hello everyone. Welcome to GeeksforGeeks. You are studying NLP article"
sent_tokenize(text)
output:
['Hello everyone.',
'Welcome to GeeksforGeeks.',
'You are studying NLP article']
Source: https://www.geeksforgeeks.org/nlp-how-tokenizing-text-sentence-words-works/
Using Spacy v3.5:
import spacy
nlp_sentencizer = spacy.blank("en")
nlp_sentencizer.add_pipe("sentencizer")
text = "How are you today? I hope you have a great day"
tokens = nlp_sentencizer(text)
[str(sent) for sent in tokens.sents]
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...
using spacy
import spacy
nlp = spacy.load('en_core_web_sm')
doc = nlp(u'This is first.This is second.This is Thired ')
for sentence in doc.sents:
print(sentence)
But if you want to do get a sentence by index Example:
#don't work
doc.sents[0]
Use
list( doc.sents)[0]
(?<!\w\.\w.)(?<![A-Z]\.)(?<=\.|\?)\s(?=[A-Z])
We should rather use this Regex to avoid situation that some shortcut will be handled as end of phrase.
© 2022 - 2024 — McMap. All rights reserved.