texturvektor

Texturvektor is a numeric vector representation of textual data designed to encode the semantic and syntactic properties of text in a continuous space. Each texturvektor is a fixed-length array of real numbers, created so that similar texts have vectors close to each other under standard distance or similarity measures such as cosine similarity.

Texturvektors can be built through various approaches. Traditional methods include bag-of-words and TF-IDF, which produce sparse, high-dimensional vectors. Dense representations arise by averaging word vectors from models such as word2vec or GloVe, or by using sentence or document embeddings from neural networks. Contextualized texturvektors from transformer models (for example, BERT or GPT encoders) generate vectors that depend on surrounding text, allowing the same token to have different representations in different contexts.

Typical practice involves normalizing vectors, selecting a dimension size (often in the hundreds or thousands), and choosing a similarity metric. Contextualized vectors tend to capture nuanced semantics but require substantial compute and data to train.

Applications include information retrieval, document clustering, text classification, semantic search, paraphrase detection, and recommendation systems. They enable scalable similarity search and evaluation of textual relevance in large corpora.

Limitations include dependence on training data, potential biases, difficulties with polysemy and rare terms, and challenges in interpreting the numerical features. High dimensionality can demand significant storage and computation, and cross-domain or cross-language use may require careful adaptation.