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• W3156649430 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." @default.
• W3156649430 created "2021-04-26" @default.
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• W3156649430 date "2021-01-01" @default.
• W3156649430 modified "2023-09-23" @default.
• W3156649430 title "Framing Word Sense Disambiguation as a Multi-Label Problem for Model-Agnostic Knowledge Integration" @default.
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• W3156649430 doi "https://doi.org/10.18653/v1/2021.eacl-main.286" @default.
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