Year: 2023
Type: conference-paper
Abstract: Large neural language models (LLMs) can be powerful tools for research in lexical semantics.We illustrate this potential using the English verb break, which has numerous senses and appears in a wide range of syntactic frames.We show that LLMs capture known sense distinctions and can be used to identify informative new sense combinations for further analysis.More generally, we argue that LLMs are aligned with lexical semantic theories in providing high-dimensional, contextually modulated representations, but LLMs' lack of discrete features and dependence on usage-based data offer a genuinely new perspective on traditional problems in lexical semantics.
Authors: Erika Petersen, Christopher Potts
Institution: Stanford University
Language: English
Subfield: Language and Linguistics
Field: Arts and Humanities
Domain: Social Sciences
Open Access status: hybrid
OpenAlex ID: W4386566491