How Can NLTK Efficiently Extract Sentences from Text, Handling Complex Linguistic Nuances?

Sentence Extraction from Text: A Comprehensive Guide

Problem: Obtain a list of sentences from a provided text file, accounting for the complexities of language, such as periods used in abbreviations and numerals.

Inefficient Regular Expression:

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

Solution Using Natural Language Toolkit (NLTK):

NLTK provides a robust solution for sentence tokenization, as demonstrated by the following code:

import nltk.data

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

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

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

# Print the tokenized sentences, separated by newlines
print('\n-----\n'.join(sentences))

Benefits of NLTK Solution:

• Comprehensive: Considers the nuances of language, such as periods in abbreviations and numerals.
• Accurate: Provides reliable sentence boundaries.
• Efficient: Not reliant on complex regular expressions.

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