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GM-CTSC at SemEval-2020 Task 1: Gaussian mixtures cross temporal similarity clustering

Cassotti, Pierluigi, Caputo, Annalina orcid logoORCID: 0000-0002-7144-8545, Polignano, Marco orcid logoORCID: 0000-0002-3939-0136 and Basile, Pierpaolo orcid logoORCID: 0000-0002-0545-1105 (2020) GM-CTSC at SemEval-2020 Task 1: Gaussian mixtures cross temporal similarity clustering. In: Fourteenth Workshop on Semantic Evaluation, Dec 2020, Barcelona (Online).

This paper describes the system proposed by the Random team for SemEval-2020 Task 1: Unsupervised Lexical Semantic Change Detection. We focus our approach on the detection problem. Given the semantics of words captured by temporal word embeddings in different time periods, we investigate the use of unsupervised methods to detect when the target word has gained or lost senses. To this end, we define a new algorithm based on Gaussian Mixture Models to cluster the target similarities computed over the two periods. We compare the proposed approach with a number of similarity-based thresholds. We found that, although the performance of the detection methods varies across the word embedding algorithms, the combination of Gaussian Mixture with Temporal Referencing resulted in our best system.
Item Type:Conference or Workshop Item (Paper)
Event Type:Workshop
Subjects:Computer Science > Computational linguistics
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Research Institutes and Centres > ADAPT
Published in: Proceedings of the Fourteenth Workshop on Semantic Evaluation. . International Committee for Computational Linguistics (ICCL).
Publisher:International Committee for Computational Linguistics (ICCL)
Official URL:https://www.aclweb.org/anthology/2020.semeval-1.7
Copyright Information:© 2020 The Authors. (CC-BY-4.0)
Funders:Science Foundation Ireland SFI 13/RC/2106, Science Foundation Ireland SFI Research Centres Programme and is co-funded under the European Regional Development Fund (ERDF) Grant # 13/RC/2106.
ID Code:25945
Deposited On:02 Jun 2021 10:38 by Annalina Caputo . Last Modified 02 Jun 2021 10:45

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