SOTAVerified

Word Sense Disambiguation

The task of Word Sense Disambiguation (WSD) consists of associating words in context with their most suitable entry in a pre-defined sense inventory. The de-facto sense inventory for English in WSD is WordNet.. For example, given the word “mouse” and the following sentence:

“A mouse consists of an object held in one's hand, with one or more buttons.”

we would assign “mouse” with its electronic device sense (the 4th sense in the WordNet sense inventory).

Papers

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Showing 1–10 of 1035 papers

TitleStatusHype
Semantic similarity estimation for domain specific data using BERT and other techniques—0
On Self-improving Token Embeddings—0
SANDWiCH: Semantical Analysis of Neighbours for Disambiguating Words in Context ad HocCode0
GlossGPT: GPT for Word Sense Disambiguation using Few-shot Chain-of-Thought PromptingCode0
Probing Semantic Routing in Large Mixture-of-Expert Models—0
TreeMatch: A Fully Unsupervised WSD System Using Dependency Knowledge on a Specific Domain—0
Fietje: An open, efficient LLM for DutchCode2
Word Sense Linking: Disambiguating Outside the Sandbox—0
Can LLMs assist with Ambiguity? A Quantitative Evaluation of various Large Language Models on Word Sense Disambiguation—0
Astro-HEP-BERT: A bidirectional language model for studying the meanings of concepts in astrophysics and high energy physics—0
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All datasetsWords in ContextSupervised:RUSSESemEval 2013 Task 12Senseval-2SensEval 3 Task 1SemEval 2007 Task 7SemEval 2007 Task 17FEWSWiC-TSVBIG-bench (Anachronisms)Knowledge-based:

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1UKBppr_w2wSenseval 268.8—Unverified
2KEFAll68—Unverified
3WSD-TMAll66.9—Unverified
4BabelfyAll65.5—Unverified
5WN 1st sense baselineAll65.2—Unverified
6UKBppr_w2w-nfAll57.5—Unverified