tokenrepresentationer

Token Representationer is a conceptual framework and set of techniques used in natural language processing (NLP) and machine learning to encode discrete units of text, known as tokens, into a numerical format that can be processed by algorithms. These tokens can be words, sub-word units (like prefixes or suffixes), or even characters, depending on the chosen tokenization strategy. The core idea is to transform linguistic data, which is inherently symbolic, into a vector space where mathematical operations can be performed.

Different methods exist for token representation. One common approach is one-hot encoding, where each unique token is assigned a unique vector with a single '1' at its corresponding position and '0's elsewhere. While simple, this method can lead to very high-dimensional and sparse vectors, especially with large vocabularies. More advanced techniques like word embeddings, such as Word2Vec, GloVe, and FastText, learn dense vector representations where tokens with similar meanings are located closer to each other in the vector space. These embeddings capture semantic and syntactic relationships between words. More recently, contextual embeddings, like those generated by transformer models such as BERT and GPT, represent tokens based on their surrounding context, allowing a single word to have different representations depending on its usage in a sentence. The choice of token representation significantly impacts the performance of downstream NLP tasks like text classification, machine translation, and sentiment analysis.