Computational Semantics

Overview

Computational semantics is the study of how the meaning of natural language can be represented and computed automatically. It sits at the intersection of linguistics, computer science, logic, and artificial intelligence, and is concerned with translating words, phrases, and sentences into formal representations that machines can store, reason over, and manipulate. Central topics include modelling word meaning, composing the meanings of phrases and sentences from their parts, capturing relations such as synonymy, entailment, and reference, and resolving ambiguity in context. Approaches range from logic-based and formal representations, which express meaning in symbolic structures, to distributional and vector-based methods that derive meaning from patterns of word use across large corpora, and increasingly to neural models that learn semantic representations from data. Computational semantics underpins applications such as machine translation, question answering, information extraction, dialogue systems, and semantic search, where systems must interpret rather than merely match text. Within Language Research, it connects theoretical accounts of meaning with the computational tools needed to process language at scale. This page gathers peer-reviewed, open-access research relevant to computational semantics and the broader computational and linguistic study of meaning within the journal's scope.

Research published in this journal

1 peer-reviewed article, ranked by relevance. Each links to its DOI.

This page summarises published research for orientation; it is not medical or professional advice.

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