Abstract
This paper describes our TemporalTeller system for SemEval Task 1: Unsupervised Lexical Semantic Change Detection. We develop a unified framework for the common semantic change detection pipelines including preprocessing, learning word embeddings, calculating vector distances and determining threshold. We also propose Gamma Quantile Threshold to distinguish between changed and stable words. Based on our system, we conduct a comprehensive comparison among BERT, Skip-gram, Temporal Referencing and alignment-based methods. Evaluation results show that Skip-gram with Temporal Referencing achieves the best performance of 66.5% classification accuracy and 51.8% Spearman’s Ranking Correlation.- Anthology ID:
- 2020.semeval-1.27
- Volume:
- Proceedings of the Fourteenth Workshop on Semantic Evaluation
- Month:
- December
- Year:
- 2020
- Address:
- Barcelona (online)
- Editors:
- Aurelie Herbelot, Xiaodan Zhu, Alexis Palmer, Nathan Schneider, Jonathan May, Ekaterina Shutova
- Venue:
- SemEval
- SIG:
- SIGLEX
- Publisher:
- International Committee for Computational Linguistics
- Note:
- Pages:
- 222–231
- Language:
- URL:
- https://aclanthology.org/2020.semeval-1.27
- DOI:
- 10.18653/v1/2020.semeval-1.27
- Cite (ACL):
- Jinan Zhou and Jiaxin Li. 2020. TemporalTeller at SemEval-2020 Task 1: Unsupervised Lexical Semantic Change Detection with Temporal Referencing. In Proceedings of the Fourteenth Workshop on Semantic Evaluation, pages 222–231, Barcelona (online). International Committee for Computational Linguistics.
- Cite (Informal):
- TemporalTeller at SemEval-2020 Task 1: Unsupervised Lexical Semantic Change Detection with Temporal Referencing (Zhou & Li, SemEval 2020)
- PDF:
- https://preview.aclanthology.org/emnlp-22-attachments/2020.semeval-1.27.pdf