contextvectoren

Context vectors are numerical representations of the linguistic context in which a word or phrase occurs. In distributional semantics, the meaning of a term is inferred from its surrounding words, following the distributional hypothesis that words appearing in similar contexts tend to have similar meanings. A context vector typically contains counts or weighted frequencies of neighboring words within a fixed window around the target term, or it may encode embeddings derived from neural models.

Count‑based methods build a high‑dimensional sparse vector from raw co‑occurrence statistics, often normalized by techniques such as TF‑IDF or positive pointwise mutual information to reduce noise. Prediction‑based approaches, such as word2vec, GloVe, or fastText, learn dense low‑dimensional embeddings by training a neural network to predict either surrounding words (CBOW) or the target word (Skip‑gram). These embeddings serve as compact context vectors that capture semantic similarities in a continuous space.

Context vectors are used for a range of NLP tasks. They can compute lexical similarity, support clustering of words or documents, and provide features for downstream supervised learning. They also enable analogy reasoning (e.g., king‑man + woman ≈ queen) and underpin many state‑of‑the‑art transformer models, where attention mechanisms operate on contextualized embeddings that are updated per sentence.

While powerful, context vectors have limitations. Sparse count vectors suffer from high dimensionality and sparsity, whereas dense embeddings may entangle multiple semantic aspects, complicating interpretability. Researchers continue to refine these representations, exploring multilingual, contextual, and graph‑based extensions to better capture nuanced meaning.