A Systematic Comparison of Contextualized Word Embeddings for Lexical Semantic Change

arXiv:2402.12011v1 Announce Type: new
Abstract: Contextualized embeddings are the preferred tool for modeling Lexical Semantic Change (LSC). Current evaluations typically focus on a specific task known as Graded Change Detection (GCD). However, performance comparison across work are often misleading due to their reliance on diverse settings. In this paper, we evaluate state-of-the-art models and approaches for GCD under equal conditions. We further break the LSC problem into Word-in-Context (WiC) and Word Sense Induction (WSI) tasks, and compare models across these …

abstract arxiv change comparison cs.cl current detection diverse embeddings focus modeling paper performance reliance semantic tool type word word embeddings work