How Can I Effectively Extract Sentences from Text Using Regular Expressions or NLTK?

Extracting Sentences using Regular Expressions

Splitting a text into sentences presents several complexities, particularly due to the presence of abbreviations and periods used in other contexts. To address this challenge, we explore various approaches.

Regular Expressions

A straightforward approach employs regular expressions. However, the provided regular expression may be inadequate as it fails to reliably handle all subtleties, including abbreviations.

Natural Language Toolkit (NLTK)

An alternative solution leverages the NLTK, a powerful library for natural language processing. NLTK's sentence tokenizer, as demonstrated in the code snippet below, effectively tokenizes text into sentences:

import nltk.data

# Load the English tokenizer
tokenizer = nltk.data.load('tokenizers/punkt/english.pickle')

# Read the text from a file
with open("test.txt") as fp:
    data = fp.read()

# Tokenize the text
sentences = tokenizer.tokenize(data)

# Print the tokenized sentences
print('\n-----\n'.join(sentences))

By employing this technique, one can effectively extract sentences from text, even those containing abbreviations and other potential pitfalls.

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