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 extended seamlessly to exploit structured knowledge from semantic networks to achieve state-of-the-art results in English all-words WSD.
Framing Word Sense Disambiguation as a Multi-Label Problem for Model-Agnostic Knowledge Integration / Conia, Simone; Navigli, Roberto. - (2021), pp. 3269-3275. (Intervento presentato al convegno European Association for Computational Linguistics tenutosi a Online) [10.18653/v1/2021.eacl-main.286].
Framing Word Sense Disambiguation as a Multi-Label Problem for Model-Agnostic Knowledge Integration
Simone Conia
Primo
;Roberto Navigli
Ultimo
2021
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 extended seamlessly to exploit structured knowledge from semantic networks to achieve state-of-the-art results in English all-words WSD.File | Dimensione | Formato | |
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