title: Word Sense Linking: Disambiguating Outside the Sandbox
summary: Word Sense Disambiguation (WSD) is the task of associating a word in a given
  context with its most suitable meaning among a set of possible candidates.
  While the task has recently witnessed renewed interest, with systems achieving
  performances above the estimated inter-annotator agreement, at the time of
  writing it still struggles to find downstream applications. We argue that one
  of the reasons behind this is the difficulty of applying WSD to plain text.
  Indeed, in the standard formulation, models work under the assumptions that a)
  all the spans to disambiguate have already been identified, and b) all the
  possible candidate senses of each span are provided, both of which are
  requirements that are far from trivial. In this work, we present a new task
  called Word Sense Linking (WSL) where, given an input text and a reference
  sense inventory, systems have to both identify which spans to disambiguate and
  then link them to their most suitable meaning.We put forward a
  transformer-based architecture for the task and thoroughly evaluate both its
  performance and those of state-of-the-art WSD systems scaled to WSL,
  iteratively relaxing the assumptions of WSD. We hope that our work will foster
  easier integration of lexical semantics into downstream applications.
paper_authors._id: 675bc870491d8c264d4c3520
paper_authors.name: Andrei Stefan Bejgu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-12-13T15:22:04.077Z
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paper_authors.fullname: Andrei Stefan Bejgu
paper_authors.type: user
paper_authors.user: andreim14
paper_authors._id: 675bc870491d8c264d4c3521
paper_authors.name: Edoardo Barba
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-12-16T11:04:35.046Z
paper_authors._id: 614dc83d8d64cfc347c28e42
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paper_authors.fullname: Edoardo Barba
paper_authors.type: user
paper_authors.user: edobobo
paper_authors._id: 675bc870491d8c264d4c3522
paper_authors.name: Luigi Procopio
paper_authors._id: 675bc870491d8c264d4c3523
paper_authors.name: Alberte Fernández-Castro
paper_authors._id: 675bc870491d8c264d4c3524
paper_authors.name: Roberto Navigli
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-12-16T11:04:59.882Z
paper_authors._id: 63845c7c99292a80134bc784
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/63845c7c99292a80134bc784/eKOPJMjm8F-JUKT_mi7Dv.png
paper_authors.fullname: Roberto Navigli
paper_authors.type: user
paper_authors.user: navigli
paper_title: Word Sense Linking: Disambiguating Outside the Sandbox
paper_summary: Word Sense Disambiguation (WSD) is the task of associating a word in a given
  context with its most suitable meaning among a set of possible candidates.
  While the task has recently witnessed renewed interest, with systems achieving
  performances above the estimated inter-annotator agreement, at the time of
  writing it still struggles to find downstream applications. We argue that one
  of the reasons behind this is the difficulty of applying WSD to plain text.
  Indeed, in the standard formulation, models work under the assumptions that a)
  all the spans to disambiguate have already been identified, and b) all the
  possible candidate senses of each span are provided, both of which are
  requirements that are far from trivial. In this work, we present a new task
  called Word Sense Linking (WSL) where, given an input text and a reference
  sense inventory, systems have to both identify which spans to disambiguate and
  then link them to their most suitable meaning.We put forward a
  transformer-based architecture for the task and thoroughly evaluate both its
  performance and those of state-of-the-art WSD systems scaled to WSL,
  iteratively relaxing the assumptions of WSD. We hope that our work will foster
  easier integration of lexical semantics into downstream applications.
paper_ai_summary: A new task called Word Sense Linking (WSL) is introduced, which involves identifying spans in text and linking them to their correct meanings, and a transformer-based architecture is proposed for this task.
paper_ai_keywords: Word Sense Disambiguation (WSD) Word Sense Linking (WSL) transformer-based architecture