Semantic Representations for NLP Using VerbNet and the Generative Lexicon.

ABSTRACT: The need for deeper semantic processing of human language by our natural language processing systems is evidenced by their still-unreliable performance on inferencing tasks, even using deep learning techniques. These tasks require the detection of subtle interactions between participants in events, of sequencing of subevents that are often not explicitly mentioned, and of changes to various participants across an event. Human beings can perform this detection even when sparse lexical items are involved, suggesting that linguistic insights into these abilities could improve NLP performance. In this article, we describe new, hand-crafted semantic representations for the lexical resource VerbNet that draw heavily on the linguistic theories about subevent semantics in the Generative Lexicon (G

SUBMITTER: Brown SW 

PROVIDER: S-EPMC9048683 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature