What's the Best Approach to Sentence Splitting Beyond Regular Expressions?

Alternatives to Regular Expressions for Sentence Splitting

Incorporating various sentence-ending punctuations along with upper case starting, a sentence splitter using regular expressions can arise as a plausible solution. However, such regular expressions often exhibit an imperfect performance when encountering the subtle placements of abbreviations that also end with a dot.

The Natural Language Toolkit (NLTK) offers a comprehensive tool for natural language processing, including a dedicated module for sentence segmentation. This module is equipped with sophisticated algorithms that can accurately split text into sentences, handling complexities such as abbreviation handling.

Implementing sentence splitting using NLTK can be achieved through the following steps:

1. Import the NLTK library into your code.
2. Load the NLTK English Punkt tokenizer, designed specifically for English language tokenization.
3. Open the text file you want to split into sentences.
4. Read the contents of the text file into a string variable.
5. Utilize the tokenizer to split the text into a list of sentences.
6. Print the resulting list of sentences, separated by newlines.

Example code:

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)))

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