January 1, 2021 Conference paper Open Access

Simone Conia; Roberto Navigli

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        "name": "Simone Conia"
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    "description": "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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    "keywords": [
      "H2020", 
      "EC", 
      "European Research Council", 
      "Consolidator Grant", 
      "European Commission", 
      "Knowmad Institut", 
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    "publication_date": "2021-01-01", 
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    "title": "Framing Word Sense Disambiguation as a Multi-Label Problem for Model-Agnostic Knowledge Integration"
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