Word Sense Linking: Disambiguating Outside the Sandbox
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.
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Andrei Stefan Bejgu
claimed_verified
2024-12-13T15:22:04.077Z
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Andrei Stefan Bejgu
user
andreim14
675bc870491d8c264d4c3521
Edoardo Barba
admin_assigned
2024-12-16T11:04:35.046Z
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Edoardo Barba
user
edobobo
675bc870491d8c264d4c3522
Luigi Procopio
675bc870491d8c264d4c3523
Alberte Fernández-Castro
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Roberto Navigli
admin_assigned
2024-12-16T11:04:59.882Z
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Roberto Navigli
user
navigli
Word Sense Linking: Disambiguating Outside the Sandbox
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.
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.
Word Sense Disambiguation (WSD) Word Sense Linking (WSL) transformer-based architecture