ChiWUG: A Graph-based Evaluation Dataset for Chinese Lexical Semantic Change Detection
Jing Chen, Emmanuele Chersoni, Dominik Schlechtweg, Jelena Prokic, Chu-Ren Huang
Abstract
Recent studies suggested that language models are efficient tools for measuring lexical semantic change. In our paper, we present the compilation of the first graph-based evaluation dataset for lexical semantic change in the context of the Chinese language, specifically covering the periods of pre- and post- Reform and Opening Up. Exploiting the existing framework DURel, we collect over 61,000 human semantic relatedness judgments for 40 targets. The inferred word usage graphs and semantic change scores provide a basis for visualization and evaluation of semantic change.- Anthology ID:
- 2023.lchange-1.10
- Volume:
- Proceedings of the 4th Workshop on Computational Approaches to Historical Language Change
- Month:
- December
- Year:
- 2023
- Address:
- Singapore
- Editors:
- Nina Tahmasebi, Syrielle Montariol, Haim Dubossarsky, Andrey Kutuzov, Simon Hengchen, David Alfter, Francesco Periti, Pierluigi Cassotti
- Venue:
- LChange
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 93–99
- Language:
- URL:
- https://aclanthology.org/2023.lchange-1.10
- DOI:
- 10.18653/v1/2023.lchange-1.10
- Cite (ACL):
- Jing Chen, Emmanuele Chersoni, Dominik Schlechtweg, Jelena Prokic, and Chu-Ren Huang. 2023. ChiWUG: A Graph-based Evaluation Dataset for Chinese Lexical Semantic Change Detection. In Proceedings of the 4th Workshop on Computational Approaches to Historical Language Change, pages 93–99, Singapore. Association for Computational Linguistics.
- Cite (Informal):
- ChiWUG: A Graph-based Evaluation Dataset for Chinese Lexical Semantic Change Detection (Chen et al., LChange 2023)
- PDF:
- https://preview.aclanthology.org/improve-issue-templates/2023.lchange-1.10.pdf