Large language models (LLMs) have materially changed natural language processing (NLP). While LLMs have shifted focus from traditional semantic-based resources, structured linguistic databases such as WordNet remain essential for precise knowledge retrieval, decision making and aiding LLM development. WordNet organizes concepts through synonym sets (synsets) and semantic links but suffers from inconsistencies, including redundant or erroneous relations. This paper investigates an approach using LLMs to aid the refinement of structured language resources, specifically WordNet, by an automation for multiple hypernymy resolution, leveraging the LLMs semantic knowledge to produce tools for aiding and evaluating manual resource improvement.

WordNet contains a fair number of synsets with multiple hyperonyms. In parent–child relations, a child can have only one parent (ancestor). Consequently, multiple hyperonymy represents distinct semantic relations. In order to reclassify the multiple hyperonyms, we define a small set of new semantic relations (such as function, origin and form) that cover the various instances of multiple hyperonyms. The synsets with multiple hyperonyms that lead to the same root and belong to the same semantic class were grouped automatically, resulting in semantic patterns that serve as a point of departure for the classification. The proposed changes are based on semantic analysis and may involve the redefinition of one or several multiple hyperonymy relations to new ones, the removal of one or several multiple hyperonymy relations, and rarely the addition of a new hyperonymy relation. As a result, we incorporate the newly defined semantic relations that resolve the former multiple hyperonymy relations and propose an updated WordNet structure without multiple hyperonyms. The resulting WordNet structure without multiple hyperonyms may be used for a variety of purposes that require proper inheritance.

The paper discusses the enrichment of WordNet data through merging of WordNet concepts and Corpus Pattern Analysis (CPA) semantic types. The 253 CPA semantic types are mapped to the respective WordNet concepts. As a result of mapping, the hyponyms of a synset to which a CPA semantic type is mapped inherit not only the respective WordNet semantic primitive but also the CPA semantic type.