A Wind of Change: Detecting and Evaluating Lexical Semantic Change across Times and Domains
This work provides a more rigorous framework for evaluating models of lexical semantic change, which is important for linguists and NLP researchers, though it is incremental as it builds on existing benchmarks.
The authors tackled the problem of detecting lexical semantic change across time and domains by conducting a large-scale evaluation that addresses superficial assessments and lack of model comparison, resulting in the extension of benchmark models on a common state-of-the-art task and successful application to domain-specific term extraction.
We perform an interdisciplinary large-scale evaluation for detecting lexical semantic divergences in a diachronic and in a synchronic task: semantic sense changes across time, and semantic sense changes across domains. Our work addresses the superficialness and lack of comparison in assessing models of diachronic lexical change, by bringing together and extending benchmark models on a common state-of-the-art evaluation task. In addition, we demonstrate that the same evaluation task and modelling approaches can successfully be utilised for the synchronic detection of domain-specific sense divergences in the field of term extraction.