Paper: AI-KU: Using Substitute Vectors and Co-Occurrence Modeling For Word Sense Induction and Disambiguation

ACL ID S13-2050
Title AI-KU: Using Substitute Vectors and Co-Occurrence Modeling For Word Sense Induction and Disambiguation
Venue Joint Conference on Lexical and Computational Semantics
Session
Year 2013
Authors

Word sense induction aims to discover differ- ent senses of a word from a corpus by us- ing unsupervised learning approaches. Once a sense inventory is obtained for an ambiguous word, word sense discrimination approaches choose the best-fitting single sense for a given context from the induced sense inventory. However, there may not be a clear distinction between one sense and another, although for a context, more than one induced sense can be suitable. Graded word sense method al- lows for labeling a word in more than one sense. In contrast to the most common ap- proach which is to apply clustering or graph partitioning on a representation of first or sec- ond order co-occurrences of a word, we pro- pose a system that creates a substitute vec- tor for each target word from the most likely...