What are the different tokenization techniques in NLP?

What are the different tokenization techniques in NLP?

In NLP, common tokenization techniques include word tokenization, subword tokenization, and character tokenization. Would you like to know more about each?

Yes, please explain word tokenization.

Word tokenization splits text into individual words, often using spaces and punctuation as delimiters. It's simple but can struggle with contractions and special cases.

What about subword tokenization?

Subword tokenization breaks words into smaller units, which helps handle rare words and reduces vocabulary size. Techniques like Byte Pair Encoding (BPE) are popular.

And character tokenization?

Character tokenization treats each character as a token. It's useful for languages with complex morphology but can lead to longer sequences.

Which technique is the best?

It depends on the application! For general text, word tokenization is often sufficient, but subword tokenization is better for handling diverse vocabularies.