Lexical Semantic Change Discovery
This work addresses the need for automated discovery of lexical semantic changes in linguistics and NLP, though it is incremental as it builds on existing models with fine-tuning.
The paper tackles the problem of discovering novel word senses over time from a full corpus vocabulary, shifting focus from change detection to change discovery, and demonstrates that fine-tuned type-based and token-based models can successfully identify new words undergoing meaning change in German data.
While there is a large amount of research in the field of Lexical Semantic Change Detection, only few approaches go beyond a standard benchmark evaluation of existing models. In this paper, we propose a shift of focus from change detection to change discovery, i.e., discovering novel word senses over time from the full corpus vocabulary. By heavily fine-tuning a type-based and a token-based approach on recently published German data, we demonstrate that both models can successfully be applied to discover new words undergoing meaning change. Furthermore, we provide an almost fully automated framework for both evaluation and discovery.