nearmeaning

Nearmeaning is a theoretical construct in linguistics and natural language processing that denotes the degree to which two linguistic units convey similar semantic content within a given context. Unlike strict synonyms, nearmeaning emphasizes context-dependent proximity: two expressions may be nearmeaning in one sentence and diverge in another, reflecting the dynamic nature of meaning in use.

In measurement, nearmeaning is typically quantified through distributional and contextual similarity. Static word embeddings and cosine similarity provide a rough estimate of nearmeaning between words, while contextualized representations from models like BERT or RoBERTa enable context-sensitive assessments of sentence or phrase similarity. Human judgments and paraphrase corpora are used to validate or calibrate these measures. Nearmeaning is particularly relevant for tasks that require flexible paraphrase recognition without assuming universal synonymy.

Applications of nearmeaning include paraphrase detection, query expansion, translation evaluation, text rewriting, and information retrieval, where two phrasings can be interchangeable within a specific context even if they are not globally interchangeable.

Nearmeaning is related to semantic similarity and synonymy but remains distinct, focusing on context-dependent proximity rather than fixed lexical relations. It is limited by polysemy, domain shift, and biases in training data, and its numeric estimates depend on the chosen representation and evaluation method.

See also: semantic similarity, paraphrase detection, distributional semantics, contextualized word representations.