Paper: Multi-Prototype Vector-Space Models of Word Meaning

ACL ID N10-1013
Title Multi-Prototype Vector-Space Models of Word Meaning
Venue Human Language Technologies
Session Main Conference
Year 2010

Current vector-space models of lexical seman- tics create a single “prototype” vector to rep- resent the meaning of a word. However, due to lexical ambiguity, encoding word mean- ing with a single vector is problematic. This paper presents a method that uses cluster- ing to produce multiple “sense-specific” vec- tors for each word. This approach provides a context-dependent vector representation of word meaning that naturally accommodates homonymy and polysemy. Experimental com- parisons to human judgements of semantic similarity for both isolated words as well as words in sentential contexts demonstrate the superiority of this approach over both proto- type and exemplar based vector-space models.