Textrepresentationer

Text representations, or textrepresentationer, are methods for converting text into numerical representations that can be processed by computer systems. They encode linguistic information in vectors used by algorithms for tasks like classification, similarity, and retrieval.

Traditional symbolic representations include one-hot encodings, bag-of-words, and TF-IDF. These approaches capture the presence or frequency of tokens but often ignore word order and semantics. They tend to produce high-dimensional, sparse vectors.

Distributed representations map words, phrases, or documents to dense vectors that capture semantic relationships. Word embeddings such as word2vec and GloVe learn from large corpora by exploiting the distributional hypothesis. These can be extended to sentences and documents via averaging, RNNs, or pooling.

Contextual representations emerged with deep learning and transformer models. Contextual embeddings produce different vectors for the same word depending on surrounding text, enabling finer-grained semantics. Models like BERT, GPT, and their multilingual variants have become standard tools in NLP.

Cross-lingual and multilingual representations enable transfer across languages. Alignments and joint training create shared vector spaces so models trained in one language can assist in others.

Common applications include information retrieval, text classification, clustering, and semantic similarity. Evaluation uses tasks like downstream performance, nearest-neighbor semantics, and intrinsic measures.

Challenges include high dimensionality in traditional methods, resource requirements for large models, interpretability, and domain adaptation. Privacy and bias considerations also affect text representations.