Subwoordtokenisatie

Subwoordtokenisatie is a technique used in natural language processing (NLP) to break down text into smaller units called subwords. Unlike traditional word tokenization, which splits text at whitespace or punctuation, subword tokenization can divide words into meaningful sub-units. This is particularly useful for handling rare words, misspelled words, and morphologically rich languages where words can have many variations.

Common subword tokenization algorithms include Byte Pair Encoding (BPE), WordPiece, and SentencePiece. BPE starts with individual characters and iteratively merges the most frequent adjacent pairs to form new subword units. WordPiece, used in models like BERT, works similarly but prioritizes merging pairs that maximize the likelihood of the training data. SentencePiece treats the input text as a sequence of Unicode characters and learns subword units directly from this sequence, allowing it to handle languages without explicit word boundaries and to segment text even without pre-tokenization.

The main advantage of subword tokenization is its ability to represent unseen words by breaking them down into known subwords. This reduces the size of the vocabulary and improves the model's generalization capabilities. For example, the word "unhappiness" might be tokenized into "un", "happi", and "ness". This allows a model that has seen "happy" and "ness" to understand "unhappiness" even if it has not encountered that specific word before. This approach has become a standard practice in many state-of-the-art NLP models, contributing significantly to their performance on a wide range of tasks.