Word Tokenization
Word tokenization splits text on whitespace or punctuation into whole words, giving readable tokens but a huge vocabulary with separate IDs for 'run', 'runs', 'running', and out-of-vocabulary issues.
What is Word Tokenization?
Word tokenization splits text on whitespace or punctuation into whole words, giving readable tokens but a huge vocabulary with separate IDs for 'run', 'runs', 'running', and out-of-vocabulary issues.
Word tokenization splits text on whitespace or punctuation into whole words, giving readable tokens but a huge vocabulary with separate IDs for 'run', 'runs', 'running', and out-of-vocabulary issues.
Where is it used?
Word-level tokenization was used in early NLP (word2vec, GloVe) and some legacy NLP pipelines; modern LLMs avoid it because of vocabulary bloat and morphology handling.
How to build it
Use `nltk.word_tokenize` or a simple `text.split()`, build a word-to-id map, and observe how quickly the vocabulary grows on a real corpus — then compare with subword methods.