This study explores the role of thematic role structures in bridging FrameNet, a computational resource for lexical semantics, with natural language processing (NLP) and linguistics. Thematic roles, which capture the relationship between a verb and its arguments, serve as crucial elements in understanding sentence structure and meaning. By examining how FrameNet categorizes these roles, the research highlights their significance in representing the semantic relationships within natural language. The study delves into how thematic role structures can improve the integration of FrameNet with NLP tools, enhancing tasks such as machine translation, information retrieval, and syntactic parsing. Through a comprehensive analysis, the paper discusses the challenges and benefits of linking these structures to natural language semantics, aiming to improve linguistic models and automated systems. The study concludes by suggesting ways to refine thematic role frameworks to further enhance the interaction between theoretical linguistics and computational applications.

Thematic role structures, Natural language processing (NLP), Lexical semantics

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