Character Tokenization

Character tokenization is a natural language processing (NLP) technique that breaks text down into individual characters or small character sequences as the basic units for analysis. It treats each character (e.g., letters, digits, punctuation) as a separate token, often used in tasks like text generation, language modeling, or handling languages with complex morphology. This approach contrasts with word or subword tokenization by operating at a finer granularity.

Also known as: Char-level tokenization, Character-level tokenization, Char tokenization, Character segmentation, Char-based tokenization

Why learn Character Tokenization?

Developers should learn character tokenization when working with languages that have large vocabularies, agglutinative structures (e.g., Turkish, Finnish), or noisy text data where word boundaries are unclear. It's particularly useful in deep learning models like RNNs or transformers for tasks such as machine translation, text classification, or building character-level language models, as it reduces out-of-vocabulary issues and handles rare words effectively.

Learning Resources

• docs: Hugging Face Tokenizers Documentation - Character Tokenization
• tutorial: Stanford CS224N Lecture Notes on Tokenization
• tutorial: Character-Level Language Modeling with RNNs Tutorial
• video: NLP Tokenization Explained (Video)
• book: Speech and Language Processing Book - Chapter on Tokenization