Abstract
In this paper, we present our contribution in SemEval-2020 Task 1: Unsupervised Lexical Semantic Change Detection, where we systematically combine existing models for unsupervised capturing of lexical semantic change across time in text corpora of German, English, Latin and Swedish. In particular, we analyze the score distribution of existing models. Then we define a general threshold, adjust it independently to each of the models and measure the models’ score reliability. Finally, using both the threshold and score reliability, we aggregate the models for the two sub- tasks: binary classification and ranking.- Anthology ID:
- 2020.semeval-1.9
- Volume:
- Proceedings of the Fourteenth Workshop on Semantic Evaluation
- Month:
- December
- Year:
- 2020
- Address:
- Barcelona (online)
- Venue:
- SemEval
- SIGs:
- SIGLEX | SIGSEM
- Publisher:
- International Committee for Computational Linguistics
- Note:
- Pages:
- 90–97
- Language:
- URL:
- https://aclanthology.org/2020.semeval-1.9
- DOI:
- 10.18653/v1/2020.semeval-1.9
- Cite (ACL):
- Efrat Amar and Chaya Liebeskind. 2020. JCT at SemEval-2020 Task 1: Combined Semantic Vector Spaces Models for Unsupervised Lexical Semantic Change Detection. In Proceedings of the Fourteenth Workshop on Semantic Evaluation, pages 90–97, Barcelona (online). International Committee for Computational Linguistics.
- Cite (Informal):
- JCT at SemEval-2020 Task 1: Combined Semantic Vector Spaces Models for Unsupervised Lexical Semantic Change Detection (Amar & Liebeskind, SemEval 2020)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/2020.semeval-1.9.pdf