In this article, you will explore various tokenization methods provided by the Natural Language Toolkit (NLTK) to process and analyse textual data. Tokenization is a crucial preprocessing step in Natural Language Processing (NLP), where text is broken down into smaller units, such as sentences or words.

Tasks

1. Open a notebook a jupyter.

2. Start by installing the NLTK library.

!pip install nltk

3. Create a Corpus which is a simple multi-line string containing two sentences.

4. Import the NLTK library along with the pre-trained models for tokenization.

Import the Natural Language Toolkit (NLTK) library and download the punkt package, which contains pre-trained models for tokenizing English text.

Imported the sent_tokenize function to see how you can convert the Corpus(paragraph) to sentences.

5. Sentence Tokenization

This tokenizer identifies sentence boundaries based on punctuation like periods (.), exclamation marks (!), etc.

6. Word tokenize where each word from the corpus will be treated as a token.

This tokenizer splits the text based on spaces and punctuation and preserves punctuation as separate tokens.

7. Using the wordpuct_tokenize() method

Here it splits punctuation marks into separate tokens and It Does not treat contractions like run’s logically, instead splitting into [‘run’, “‘”, ‘s’] also each punctuation mark (‘, !, .) is considered a standalone token.

Whereas in word_tokenize

• Handles contractions like run’s by splitting into [‘run’, “‘s”].
• Treats punctuation (!, ., ,) as separate tokens but attaches them logically to sentence structure.
• Each word, including punctuations and abbreviations, is treated meaningfully.

8. Using the TreebankWordTokenizer: It uses regular expressions to tokenize text, assuming that the text has already been segmented into sentences.

This tokenizer is particularly adept at handling English contractions and punctuation, ensuring that words like “don’t” are correctly split into “do” and “n’t”.

So you have explored different tokenization techniques where,

• For sentence splitting: Use sent_tokenize.
• For word tokenization with context awareness: Use word_tokenize.
• For simple word and punctuation separation: Use wordpunct_tokenize.
• For linguistically accurate tasks (e.g., parsing): Use TreebankWordTokenizer.