@inproceedings{cuba-gyllensten-etal-2020-sensecluster,
title = "{S}ense{C}luster at {S}em{E}val-2020 Task 1: Unsupervised Lexical Semantic Change Detection",
author = "Cuba Gyllensten, Amaru and
Gogoulou, Evangelia and
Ekgren, Ariel and
Sahlgren, Magnus",
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-1.12",
doi = "10.18653/v1/2020.semeval-1.12",
pages = "112--118",
abstract = "We (Team Skurt) propose a simple method to detect lexical semantic change by clustering contextualized embeddings produced by XLM-R, using K-Means++. The basic idea is that contextualized embeddings that encode the same sense are located in close proximity in the embedding space. Our approach is both simple and generic, but yet performs relatively good in both sub-tasks of SemEval-2020 Task 1. We hypothesize that the main shortcoming of our method lies in the simplicity of the clustering method used.",
}
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<abstract>We (Team Skurt) propose a simple method to detect lexical semantic change by clustering contextualized embeddings produced by XLM-R, using K-Means++. The basic idea is that contextualized embeddings that encode the same sense are located in close proximity in the embedding space. Our approach is both simple and generic, but yet performs relatively good in both sub-tasks of SemEval-2020 Task 1. We hypothesize that the main shortcoming of our method lies in the simplicity of the clustering method used.</abstract>
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%0 Conference Proceedings
%T SenseCluster at SemEval-2020 Task 1: Unsupervised Lexical Semantic Change Detection
%A Cuba Gyllensten, Amaru
%A Gogoulou, Evangelia
%A Ekgren, Ariel
%A Sahlgren, Magnus
%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 cuba-gyllensten-etal-2020-sensecluster
%X We (Team Skurt) propose a simple method to detect lexical semantic change by clustering contextualized embeddings produced by XLM-R, using K-Means++. The basic idea is that contextualized embeddings that encode the same sense are located in close proximity in the embedding space. Our approach is both simple and generic, but yet performs relatively good in both sub-tasks of SemEval-2020 Task 1. We hypothesize that the main shortcoming of our method lies in the simplicity of the clustering method used.
%R 10.18653/v1/2020.semeval-1.12
%U https://aclanthology.org/2020.semeval-1.12
%U https://doi.org/10.18653/v1/2020.semeval-1.12
%P 112-118
Markdown (Informal)
[SenseCluster at SemEval-2020 Task 1: Unsupervised Lexical Semantic Change Detection](https://aclanthology.org/2020.semeval-1.12) (Cuba Gyllensten et al., SemEval 2020)
ACL