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
Papers
Showing 101–107 of 107 papers
Benchmark Results
| # | Model | Metric | Claimed | Verified | Status |
|---|---|---|---|---|---|
| 1 | BERT+DP | F-Score | 71.3 | — | Unverified |
| 2 | AutoSense | F-Score | 61.7 | — | Unverified |
| 3 | LDA | F-Score | 60.7 | — | Unverified |
| 4 | SE-WSI-fix | F-Score | 55.1 | — | Unverified |
| 5 | BNP-HC | F-Score | 23.1 | — | Unverified |