Understanding Tokenization
Tokenization is the process of breaking down text into individual units, known as tokens. This is a crucial first step in many NLP tasks.
Common tokenization methods include:
- Whitespace Tokenization: Simple splitting by spaces.
- Regular Expression Tokenization: More sophisticated tokenization using regular expressions.
- Subword Tokenization (e.g., Byte Pair Encoding - BPE): Effective for handling rare words and out-of-vocabulary terms.
Stemming and Lemmatization
Stemming and lemmatization are techniques for reducing words to their root form.
- Stemming: A heuristic process that chops off the ends of words.
- Lemmatization: A more sophisticated process that considers the context of the word and returns the dictionary form.
Transformer Models
Transformer models, such as BERT and GPT, have revolutionized NLP. They are based on the self-attention mechanism and have achieved state-of-the-art results on many NLP tasks.