@inproceedings{conia-navigli-2021-framing,
title = "Framing Word Sense Disambiguation as a Multi-Label Problem for Model-Agnostic Knowledge Integration",
author = "Conia, Simone and
Navigli, Roberto",
editor = "Merlo, Paola and
Tiedemann, Jorg and
Tsarfaty, Reut",
booktitle = "Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume",
month = apr,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.eacl-main.286",
doi = "10.18653/v1/2021.eacl-main.286",
pages = "3269--3275",
abstract = "Recent studies treat Word Sense Disambiguation (WSD) as a single-label classification problem in which one is asked to choose only the best-fitting sense for a target word, given its context. However, gold data labelled by expert annotators suggest that maximizing the probability of a single sense may not be the most suitable training objective for WSD, especially if the sense inventory of choice is fine-grained. In this paper, we approach WSD as a multi-label classification problem in which multiple senses can be assigned to each target word. Not only does our simple method bear a closer resemblance to how human annotators disambiguate text, but it can also be seamlessly extended to exploit structured knowledge from semantic networks to achieve state-of-the-art results in English all-words WSD.",
}
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%0 Conference Proceedings
%T Framing Word Sense Disambiguation as a Multi-Label Problem for Model-Agnostic Knowledge Integration
%A Conia, Simone
%A Navigli, Roberto
%Y Merlo, Paola
%Y Tiedemann, Jorg
%Y Tsarfaty, Reut
%S Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume
%D 2021
%8 April
%I Association for Computational Linguistics
%C Online
%F conia-navigli-2021-framing
%X Recent studies treat Word Sense Disambiguation (WSD) as a single-label classification problem in which one is asked to choose only the best-fitting sense for a target word, given its context. However, gold data labelled by expert annotators suggest that maximizing the probability of a single sense may not be the most suitable training objective for WSD, especially if the sense inventory of choice is fine-grained. In this paper, we approach WSD as a multi-label classification problem in which multiple senses can be assigned to each target word. Not only does our simple method bear a closer resemblance to how human annotators disambiguate text, but it can also be seamlessly extended to exploit structured knowledge from semantic networks to achieve state-of-the-art results in English all-words WSD.
%R 10.18653/v1/2021.eacl-main.286
%U https://aclanthology.org/2021.eacl-main.286
%U https://doi.org/10.18653/v1/2021.eacl-main.286
%P 3269-3275
Markdown (Informal)
[Framing Word Sense Disambiguation as a Multi-Label Problem for Model-Agnostic Knowledge Integration](https://aclanthology.org/2021.eacl-main.286) (Conia & Navigli, EACL 2021)
ACL