Time-Out: Temporal Referencing for Robust Modeling of Lexical Semantic Change
Haim Dubossarsky, Simon Hengchen, Nina Tahmasebi, Dominik Schlechtweg
Abstract
State-of-the-art models of lexical semantic change detection suffer from noise stemming from vector space alignment. We have empirically tested the Temporal Referencing method for lexical semantic change and show that, by avoiding alignment, it is less affected by this noise. We show that, trained on a diachronic corpus, the skip-gram with negative sampling architecture with temporal referencing outperforms alignment models on a synthetic task as well as a manual testset. We introduce a principled way to simulate lexical semantic change and systematically control for possible biases.- Anthology ID:
- P19-1044
- Volume:
- Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
- Month:
- July
- Year:
- 2019
- Address:
- Florence, Italy
- Editors:
- Anna Korhonen, David Traum, Lluís Màrquez
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 457–470
- Language:
- URL:
- https://aclanthology.org/P19-1044
- DOI:
- 10.18653/v1/P19-1044
- Cite (ACL):
- Haim Dubossarsky, Simon Hengchen, Nina Tahmasebi, and Dominik Schlechtweg. 2019. Time-Out: Temporal Referencing for Robust Modeling of Lexical Semantic Change. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 457–470, Florence, Italy. Association for Computational Linguistics.
- Cite (Informal):
- Time-Out: Temporal Referencing for Robust Modeling of Lexical Semantic Change (Dubossarsky et al., ACL 2019)
- PDF:
- https://preview.aclanthology.org/proper-vol2-ingestion/P19-1044.pdf
- Code
- Garrafao/TemporalReferencing