Paper: A Mixture Model with Sharing for Lexical Semantics

ACL ID D10-1114
Title A Mixture Model with Sharing for Lexical Semantics
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2010

We introduce tiered clustering, a mixture model capable of accounting for varying de- grees of shared (context-independent) fea- ture structure, and demonstrate its applicabil- ity to inferring distributed representations of word meaning. Common tasks in lexical se- mantics such as word relatedness or selec- tional preference can benefit from modeling such structure: Polysemous word usage is of- ten governed by some common background metaphoric usage (e.g. the senses of line or run), and likewise modeling the selectional preference of verbs relies on identifying com- monalities shared by their typical arguments. Tiered clustering can also be viewed as a form of soft feature selection, where features that do not contribute meaningfully to the clustering can be excluded. We demonstrate the a...