January 1, 2021 Conference paper Open Access

Simone Conia; Roberto Navigli

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  "@id": "https://doi.org/10.18653/v1/2021.eacl-main.286", 
  "@type": "ScholarlyArticle", 
  "creator": [
    {
      "@type": "Person", 
      "name": "Simone Conia"
    }, 
    {
      "@type": "Person", 
      "name": "Roberto Navigli"
    }
  ], 
  "datePublished": "2021-01-01", 
  "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.", 
  "headline": "Framing Word Sense Disambiguation as a Multi-Label Problem for Model-Agnostic Knowledge Integration", 
  "identifier": "https://doi.org/10.18653/v1/2021.eacl-main.286", 
  "image": "https://zenodo.org/static/img/logos/zenodo-gradient-round.svg", 
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  "keywords": [
    "H2020", 
    "EC", 
    "European Research Council", 
    "Consolidator Grant", 
    "European Commission", 
    "Knowmad Institut", 
    "Digital Humanities and Cultural Heritage"
  ], 
  "license": "", 
  "name": "Framing Word Sense Disambiguation as a Multi-Label Problem for Model-Agnostic Knowledge Integration", 
  "url": "https://www.openaccessrepository.it/record/115335"
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