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
Showing 1–10 of 1035 papers
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
| # | Model | Metric | Claimed | Verified | Status |
|---|---|---|---|---|---|
| 1 | Human Benchmark | Accuracy | 0.81 | — | Unverified |
| 2 | ruT5-large-finetune | Accuracy | 0.74 | — | Unverified |
| 3 | RuBERT conversational | Accuracy | 0.73 | — | Unverified |
| 4 | RuBERT plain | Accuracy | 0.73 | — | Unverified |
| 5 | ruRoberta-large finetune | Accuracy | 0.72 | — | Unverified |
| 6 | ruBert-base finetune | Accuracy | 0.71 | — | Unverified |
| 7 | Multilingual Bert | Accuracy | 0.69 | — | Unverified |
| 8 | ruT5-base-finetune | Accuracy | 0.68 | — | Unverified |
| 9 | ruBert-large finetune | Accuracy | 0.68 | — | Unverified |
| 10 | SBERT_Large_mt_ru_finetuning | Accuracy | 0.66 | — | Unverified |