January 1, 2021 Conference paper Open Access

Simone Conia; Roberto Navigli

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  <identifier identifierType="URL">https://www.openaccessrepository.it/record/115335</identifier>
  <creators>
    <creator>
      <creatorName>Simone Conia</creatorName>
    </creator>
    <creator>
      <creatorName>Roberto Navigli</creatorName>
    </creator>
  </creators>
  <titles>
    <title>Framing Word Sense Disambiguation as a Multi-Label Problem for Model-Agnostic Knowledge Integration</title>
  </titles>
  <publisher>INFN Open Access Repository</publisher>
  <publicationYear>2021</publicationYear>
  <subjects>
    <subject>H2020</subject>
    <subject>EC</subject>
    <subject>European Research Council</subject>
    <subject>Consolidator Grant</subject>
    <subject>European Commission</subject>
    <subject>Knowmad Institut</subject>
    <subject>Digital Humanities and Cultural Heritage</subject>
  </subjects>
  <dates>
    <date dateType="Issued">2021-01-01</date>
  </dates>
  <resourceType resourceTypeGeneral="Text">Conference paper</resourceType>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://www.openaccessrepository.it/record/115335</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsIdenticalTo">10.18653/v1/2021.eacl-main.286</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsPartOf">https://www.openaccessrepository.it/communities/itmirror</relatedIdentifier>
  </relatedIdentifiers>
  <rightsList>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
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  <descriptions>
    <description descriptionType="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.</description>
  </descriptions>
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