Subwoordmethoden

Subwoordmethoden, also known as subword tokenization, are techniques used in natural language processing (NLP) to break down words into smaller units, or subwords. This contrasts with traditional word-level tokenization, which treats each word as a distinct token. Subword methods are particularly useful for handling morphologically rich languages, out-of-vocabulary (OOV) words, and improving the generalization capabilities of NLP models.

One of the most popular subword methods is Byte Pair Encoding (BPE). BPE starts with a vocabulary of individual characters and iteratively merges the most frequent pair of adjacent characters or subwords until a desired vocabulary size is reached. This results in a vocabulary that includes common characters, frequent subword units, and whole words. Another prominent method is WordPiece, used in models like BERT. WordPiece is similar to BPE but uses a likelihood-based criterion for merging subwords, prioritizing merges that maximize the likelihood of the training data. SentencePiece is another approach that treats text as a sequence of Unicode characters and learns subword units directly from raw text, without pre-tokenization. This allows it to handle spaces as characters and operate on languages without explicit word boundaries.

The primary advantage of subword methods is their ability to represent rare or unseen words by composing them from known subword units. For instance, the word "unhappiness" might be broken down into "un", "happi", and "ness". This allows models to infer the meaning of new words based on their constituent parts, reducing the OOV problem and improving performance on various NLP tasks like machine translation, text classification, and question answering.