@inproceedings{gomez-adorno-etal-2020-mineriaunam,
title = "{M}ineria{UNAM} at {S}em{E}val-2020 Task 3: Predicting Contextual {W}ord{S}imilarity Using a Centroid Based Approach and Word Embeddings",
author = "Gomez-Adorno, Helena and
Bel-Enguix, Gemma and
Reyes-Maga{\~n}a, Jorge and
Moreno, Benjam{\'\i}n and
Casillas, Ram{\'o}n and
Vargas, Daniel",
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-1.17",
doi = "10.18653/v1/2020.semeval-1.17",
pages = "150--157",
abstract = "This paper presents our systems to solve Task 3 of Semeval-2020, which aims to predict the effect that context has on human perception of similarity of words. The task consists of two subtasks in English, Croatian, Finnish, and Slovenian: (1) predicting the change of similarity and (2) predicting the human scores of similarity, both of them for a pair of words within two different contexts. We tackled the problem by developing two systems, the first one uses a centroid approach and word vectors. The second one uses the ELMo language model, which is trained for each pair of words with the given context. Our approach achieved the highest score in subtask 2 for the English language.",
}
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%0 Conference Proceedings
%T MineriaUNAM at SemEval-2020 Task 3: Predicting Contextual WordSimilarity Using a Centroid Based Approach and Word Embeddings
%A Gomez-Adorno, Helena
%A Bel-Enguix, Gemma
%A Reyes-Magaña, Jorge
%A Moreno, Benjamín
%A Casillas, Ramón
%A Vargas, Daniel
%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 gomez-adorno-etal-2020-mineriaunam
%X This paper presents our systems to solve Task 3 of Semeval-2020, which aims to predict the effect that context has on human perception of similarity of words. The task consists of two subtasks in English, Croatian, Finnish, and Slovenian: (1) predicting the change of similarity and (2) predicting the human scores of similarity, both of them for a pair of words within two different contexts. We tackled the problem by developing two systems, the first one uses a centroid approach and word vectors. The second one uses the ELMo language model, which is trained for each pair of words with the given context. Our approach achieved the highest score in subtask 2 for the English language.
%R 10.18653/v1/2020.semeval-1.17
%U https://aclanthology.org/2020.semeval-1.17
%U https://doi.org/10.18653/v1/2020.semeval-1.17
%P 150-157
Markdown (Informal)
[MineriaUNAM at SemEval-2020 Task 3: Predicting Contextual WordSimilarity Using a Centroid Based Approach and Word Embeddings](https://aclanthology.org/2020.semeval-1.17) (Gomez-Adorno et al., SemEval 2020)
ACL