@inproceedings{hadiwinoto-etal-2019-improved,
title = "Improved Word Sense Disambiguation Using Pre-Trained Contextualized Word Representations",
author = "Hadiwinoto, Christian and
Ng, Hwee Tou and
Gan, Wee Chung",
editor = "Inui, Kentaro and
Jiang, Jing and
Ng, Vincent and
Wan, Xiaojun",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
month = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D19-1533",
doi = "10.18653/v1/D19-1533",
pages = "5297--5306",
abstract = "Contextualized word representations are able to give different representations for the same word in different contexts, and they have been shown to be effective in downstream natural language processing tasks, such as question answering, named entity recognition, and sentiment analysis. However, evaluation on word sense disambiguation (WSD) in prior work shows that using contextualized word representations does not outperform the state-of-the-art approach that makes use of non-contextualized word embeddings. In this paper, we explore different strategies of integrating pre-trained contextualized word representations and our best strategy achieves accuracies exceeding the best prior published accuracies by significant margins on multiple benchmark WSD datasets.",
}
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<abstract>Contextualized word representations are able to give different representations for the same word in different contexts, and they have been shown to be effective in downstream natural language processing tasks, such as question answering, named entity recognition, and sentiment analysis. However, evaluation on word sense disambiguation (WSD) in prior work shows that using contextualized word representations does not outperform the state-of-the-art approach that makes use of non-contextualized word embeddings. In this paper, we explore different strategies of integrating pre-trained contextualized word representations and our best strategy achieves accuracies exceeding the best prior published accuracies by significant margins on multiple benchmark WSD datasets.</abstract>
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%0 Conference Proceedings
%T Improved Word Sense Disambiguation Using Pre-Trained Contextualized Word Representations
%A Hadiwinoto, Christian
%A Ng, Hwee Tou
%A Gan, Wee Chung
%Y Inui, Kentaro
%Y Jiang, Jing
%Y Ng, Vincent
%Y Wan, Xiaojun
%S Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F hadiwinoto-etal-2019-improved
%X Contextualized word representations are able to give different representations for the same word in different contexts, and they have been shown to be effective in downstream natural language processing tasks, such as question answering, named entity recognition, and sentiment analysis. However, evaluation on word sense disambiguation (WSD) in prior work shows that using contextualized word representations does not outperform the state-of-the-art approach that makes use of non-contextualized word embeddings. In this paper, we explore different strategies of integrating pre-trained contextualized word representations and our best strategy achieves accuracies exceeding the best prior published accuracies by significant margins on multiple benchmark WSD datasets.
%R 10.18653/v1/D19-1533
%U https://aclanthology.org/D19-1533
%U https://doi.org/10.18653/v1/D19-1533
%P 5297-5306
Markdown (Informal)
[Improved Word Sense Disambiguation Using Pre-Trained Contextualized Word Representations](https://aclanthology.org/D19-1533) (Hadiwinoto et al., EMNLP-IJCNLP 2019)
ACL