Tokenization in Natural Language Processing

Tokenization is a crucial step in Natural Language Processing (NLP) that involves breaking down text into individual units or tokens. These tokens can be words, sentences, or even smaller subword units, depending on the requirements of the analysis or processing.

There are various tokenization techniques used in NLP, such as:

• Whitespace Tokenization: This simple technique splits text based on whitespace characters, such as spaces and tabs. However, it doesn't handle punctuation marks or other special characters well.
• Word Tokenization: This technique breaks text into individual words. It handles punctuation marks and special characters, but it may not handle compound words or contractions correctly.
• Subword Tokenization: This technique splits text into smaller subword units, which can be useful for languages with complex word structures or for handling out-of-vocabulary words.

Tokenization plays a vital role in many NLP tasks, such as:

• Machine Translation: Tokenizing sentences helps in aligning source and target language tokens for translation.
• Part-of-speech Tagging: Tokenization allows for accurate tagging of words with their corresponding parts of speech.
• Sentiment Analysis: Breaking down text into tokens helps in analyzing sentiment at a granular level.

Tokenization is the foundation for further NLP tasks like lemmatization, stemming, and named entity recognition.