Tekstrelevans

Tekstrelevans is a measure of how relevant a text is to a given query or information need. In information retrieval, tekstrelevans describes the degree to which document content addresses the user's intent. Relevance depends on topical alignment, specificity, and usefulness, and can be evaluated with user feedback or relevance judgments.

Common approaches quantify tekstrelevans with lexical and semantic signals. Lexical methods include bag-of-words representations and scoring functions such as TF-IDF or BM25, which assess term frequency and document frequency and produce a ranking score. Vector-space methods use cosine similarity between a query vector and a document vector. Semantic and contextual approaches use embeddings from neural models or transformer-based architectures to capture synonyms, paraphrases, and contextual meaning, enabling semantic similarity-based ranking.

A typical retrieval pipeline includes preprocessing of text, building an inverted index for fast lexical matching, performing an initial retrieval to obtain a candidate set, and applying a ranking model to score documents according to tekstrelevans. Features may include term overlap, term proximity, query/document length normalization, freshness, and user signals.

Evaluation of tekstrelevans relies on labeled relevance judgments and metrics such as precision, recall, mean average precision, and NDCG. Real-world systems balance relevance with efficiency and diversity, to present a usable result list.

Challenges include handling ambiguity, synonymy, negation, and polysemy; domain-specific language; noisy queries; and multilingual or cross-lingual retrieval. Tekstrelevans remains a central concept in search, information retrieval research, and natural language processing, underpinning how systems rank and present information to users.