natural-language-processing-8
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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Most implemented papers
Breaking Sticks and Ambiguities with Adaptive Skip-gram
D. Vetrov, Sergey Bartunov, A. Osokin, D. Kondrashkin•Tue Feb 24 2015
The Adaptive Skip-gram model is proposed which is a nonparametric Bayesian extension of Skip- Gram capable to automatically learn the required number of representations for all words at desired semantic resolution and derives efficient online variational learning algorithm for the model and empirically demonstrates its efficiency on word-sense induction task.
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