RIJP at SemEval-2020 Task 1: Gaussian-based Embeddings for Semantic Change Detection

Ran Iwamoto, Masahiro Yukawa


Abstract
This paper describes the model proposed and submitted by our RIJP team to SemEval 2020 Task1: Unsupervised Lexical Semantic Change Detection. In the model, words are represented by Gaussian distributions. For Subtask 1, the model achieved average scores of 0.51 and 0.70 in the evaluation and post-evaluation processes, respectively. The higher score in the post-evaluation process than that in the evaluation process was achieved owing to appropriate parameter tuning. The results indicate that the proposed Gaussian-based embedding model is able to express semantic shifts while having a low computational
Anthology ID:
2020.semeval-1.10
Volume:
Proceedings of the Fourteenth Workshop on Semantic Evaluation
Month:
December
Year:
2020
Address:
Barcelona (online)
Venues:
COLING | SemEval
SIGs:
SIGLEX | SIGSEM
Publisher:
International Committee for Computational Linguistics
Note:
Pages:
98–104
Language:
URL:
https://aclanthology.org/2020.semeval-1.10
DOI:
10.18653/v1/2020.semeval-1.10
Bibkey:
Cite (ACL):
Ran Iwamoto and Masahiro Yukawa. 2020. RIJP at SemEval-2020 Task 1: Gaussian-based Embeddings for Semantic Change Detection. In Proceedings of the Fourteenth Workshop on Semantic Evaluation, pages 98–104, Barcelona (online). International Committee for Computational Linguistics.
Cite (Informal):
RIJP at SemEval-2020 Task 1: Gaussian-based Embeddings for Semantic Change Detection (Iwamoto & Yukawa, SemEval 2020)
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PDF:
https://preview.aclanthology.org/update-css-js/2020.semeval-1.10.pdf