GM-CTSC at SemEval-2020 Task 1: Gaussian Mixtures Cross Temporal Similarity Clustering

Pierluigi Cassotti, Annalina Caputo, Marco Polignano, Pierpaolo Basile

Abstract
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.
Anthology ID:
2020.semeval-1.7
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:
74–80
Language:
URL:
https://aclanthology.org/2020.semeval-1.7
DOI:
10.18653/v1/2020.semeval-1.7
Bibkey:
Cite (ACL):
Pierluigi Cassotti, Annalina Caputo, Marco Polignano, and Pierpaolo Basile. 2020. GM-CTSC at SemEval-2020 Task 1: Gaussian Mixtures Cross Temporal Similarity Clustering. In Proceedings of the Fourteenth Workshop on Semantic Evaluation, pages 74–80, Barcelona (online). International Committee for Computational Linguistics.
Cite (Informal):
GM-CTSC at SemEval-2020 Task 1: Gaussian Mixtures Cross Temporal Similarity Clustering (Cassotti et al., SemEval 2020)
Copy Citation:
PDF:
https://preview.aclanthology.org/nschneid-patch-3/2020.semeval-1.7.pdf
PDF Search