Effects of Pre- and Post-Processing on type-based Embeddings in Lexical Semantic Change Detection
Jens Kaiser, Sinan Kurtyigit, Serge Kotchourko, Dominik Schlechtweg
Abstract
Lexical semantic change detection is a new and innovative research field. The optimal fine-tuning of models including pre- and post-processing is largely unclear. We optimize existing models by (i) pre-training on large corpora and refining on diachronic target corpora tackling the notorious small data problem, and (ii) applying post-processing transformations that have been shown to improve performance on synchronic tasks. Our results provide a guide for the application and optimization of lexical semantic change detection models across various learning scenarios.- Anthology ID:
- 2021.eacl-main.10
- Volume:
- Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume
- Month:
- April
- Year:
- 2021
- Address:
- Online
- Editors:
- Paola Merlo, Jorg Tiedemann, Reut Tsarfaty
- Venue:
- EACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 125–137
- Language:
- URL:
- https://aclanthology.org/2021.eacl-main.10
- DOI:
- 10.18653/v1/2021.eacl-main.10
- Cite (ACL):
- Jens Kaiser, Sinan Kurtyigit, Serge Kotchourko, and Dominik Schlechtweg. 2021. Effects of Pre- and Post-Processing on type-based Embeddings in Lexical Semantic Change Detection. In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pages 125–137, Online. Association for Computational Linguistics.
- Cite (Informal):
- Effects of Pre- and Post-Processing on type-based Embeddings in Lexical Semantic Change Detection (Kaiser et al., EACL 2021)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-1/2021.eacl-main.10.pdf
- Code
- Garrafao/LSCDetection