Framing Word Sense Disambiguation as a Multi-Label Problem for Model-Agnostic Knowledge Integration
Simone Conia, Roberto Navigli · 2021
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 finegrained.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 stateof-the-art results in English all-words WSD.