Senseval 2: 80.8Senseval 3: 79.0SemEval 2007: 75.2SemEval 2013: 80.7
Papers with Code · CC BY-SA 4.0
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).
개요는 각 항목의 일부입니다. 위 탭에서 전체 기록을 페이지별로 탐색할 수 있습니다.
벤치마크
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Dataset기록된 모델 예시MetricPaperCode
transformers
Task 1 Accuracy: all: 77.8Task 1 Accuracy: general purpose: 75.2Task 1 Accuracy: domain specific: 81.0Task 3 Accuracy: all: 71.9
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데이터셋
17개논문
Senseval 2: 82.7Senseval 3: 81.0SemEval 2007: 78.5SemEval 2013: 85.2
Senseval 2: 82.5Senseval 3: 80.2SemEval 2007: 78.5SemEval 2013: 82.3
Senseval 2: 81.7Senseval 3: 77.8SemEval 2007: 76.3SemEval 2013: 82.2
Senseval 2: 86.1Senseval 3: 82.9SemEval 2007: 76.2SemEval 2013: 75.4
Senseval 2: 79.4Senseval 3: 77.4SemEval 2007: 74.5SemEval 2013: 79.7
Senseval 2: 87.8Senseval 3: 85.7SemEval 2007: 80.9SemEval 2013: 92.6
Senseval 2: 79.7Senseval 3: 77.8SemEval 2007: 73.4SemEval 2013: 78.7
Senseval 2: 79.6Senseval 3: 77.3SemEval 2007: 73.0SemEval 2013: 79.4
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