natural-language-processing-20
Word Sense Induction
3260 papers • 126 benchmarks • 313 datasets
Word sense induction (WSI) is widely known as the “unsupervised version” of WSD. The problem states as: Given a target word (e.g., “cold”) and a collection of sentences (e.g., “I caught a cold”, “The weather is cold”) that use the word, cluster the sentences according to their different senses/meanings. We do not need to know the sense/meaning of each cluster, but sentences inside a cluster should have used the target words with the same sense. Description from NLP Progress
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SemEval 2010 WSI
SemEval 2010 WSI
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