F-Score: 61.7V-Measure: 9.8AVG: 24.59
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Word Sense Induction
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
논문
F-Score: 23.1V-Measure: 21.4AVG: 22.23
F-Score: 55.1V-Measure: 9.8AVG: 23.24
F-Score: 71.3V-Measure: 40.4AVG: 53.6
F-Score: 60.7V-Measure: 4.4AVG: 16.34