@inproceedings{iwamoto-yukawa-2020-rijp,
title = "{RIJP} at {S}em{E}val-2020 Task 1: {G}aussian-based Embeddings for Semantic Change Detection",
author = "Iwamoto, Ran and
Yukawa, Masahiro",
editor = "Herbelot, Aurelie and
Zhu, Xiaodan and
Palmer, Alexis and
Schneider, Nathan and
May, Jonathan and
Shutova, Ekaterina",
booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
month = dec,
year = "2020",
address = "Barcelona (online)",
publisher = "International Committee for Computational Linguistics",
url = "https://aclanthology.org/2020.semeval-1.10",
doi = "10.18653/v1/2020.semeval-1.10",
pages = "98--104",
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",
}
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<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</abstract>
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%0 Conference Proceedings
%T RIJP at SemEval-2020 Task 1: Gaussian-based Embeddings for Semantic Change Detection
%A Iwamoto, Ran
%A Yukawa, Masahiro
%Y Herbelot, Aurelie
%Y Zhu, Xiaodan
%Y Palmer, Alexis
%Y Schneider, Nathan
%Y May, Jonathan
%Y Shutova, Ekaterina
%S Proceedings of the Fourteenth Workshop on Semantic Evaluation
%D 2020
%8 December
%I International Committee for Computational Linguistics
%C Barcelona (online)
%F iwamoto-yukawa-2020-rijp
%X 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
%R 10.18653/v1/2020.semeval-1.10
%U https://aclanthology.org/2020.semeval-1.10
%U https://doi.org/10.18653/v1/2020.semeval-1.10
%P 98-104
Markdown (Informal)
[RIJP at SemEval-2020 Task 1: Gaussian-based Embeddings for Semantic Change Detection](https://aclanthology.org/2020.semeval-1.10) (Iwamoto & Yukawa, SemEval 2020)
ACL