Tokenization is the process of breaking down text into smaller units called tokens, which are typically words, subwords, or characters. In NLP, tokens serve as the foundational elements for models to analyze and process language. For example, the sentence “I love NLP!” might be split into tokens like ["I", "love", "NLP", “!”]. This step is critical because raw text is unstructured and models require numerical or standardized input. Tokenization helps convert unstructured text into a format that algorithms can work with, such as sequences of integers or vectors.
The method of tokenization varies based on the task and language. Simple approaches split text by whitespace and punctuation, but this can fail for languages without clear word boundaries (e.g., Chinese) or for handling contractions like “don’t” (split into ["do", “n’t”]). Advanced techniques, such as subword tokenization (used in models like BERT), break rare words into smaller meaningful units. For instance, “unhappiness” might become ["un", “happiness”], allowing the model to recognize shared components across words. Libraries like spaCy or Hugging Face’s tokenizers implement rules or machine learning to handle edge cases, such as hyphenated words or URLs, ensuring consistency.
Developers must consider trade-offs when choosing a tokenization strategy. Word-based tokenization can lead to large vocabularies for morphologically rich languages (e.g., Turkish), while subword approaches balance vocabulary size and out-of-vocabulary handling. Character-level tokenization avoids vocabulary issues entirely but loses semantic meaning. For example, translating “cat” at the character level would treat "c", "a", and “t” separately, which might not capture the word’s meaning. Tokenization also impacts computational efficiency: longer sequences from character tokens require more memory, while word tokens reduce sequence length but increase vocabulary. Choosing the right method depends on the language, task, and model constraints.