semantisets

Semantisets are structured collections of terms that cluster around a core concept to capture semantic relatedness. They are used as operational representations of meaning in linguistics, information retrieval, and natural language processing, providing a compact map of terms that express or relate to a given concept.

Formally, a semanset S for a target concept C is a finite set of terms t1, t2, ..., tn such that each term ti is semantically related to C above a chosen threshold. Relationships can be assessed via embedding-based similarity, lexical databases, or corpus co-occurrence statistics. A semiset may include synonyms, hypernyms, hyponyms, topical associates, and related collocations, and may be designed with varying degrees of inclusiveness or specificity.

Semantisets are typically constructed through automatic methods, manual curation, or a combination of both. Automatic approaches draw on distributional semantics, knowledge graphs, and lexical resources (for example, WordNet or ConceptNet) to identify candidate terms. Distinctions are made between lexical semisets, which focus on single-word or multiword terms, and contextual semisets, which emphasize terms that tend to appear in similar contexts.

Examples are helpful for illustration. For the concept “dog,” a semanset might include dog, canine, puppy, hound, pet. For “car,” it could include automobile, vehicle, sedan, hatchback, truck. The exact composition depends on the domain, the desired granularity, and the chosen similarity criteria.

Applications of semantisets include semantic search and query expansion, word sense disambiguation, ontology learning, and knowledge integration. They provide a practical, operational view of meaning that complements more formal semantic frameworks.