Paper: Measuring Word Relatedness Using Heterogeneous Vector Space Models

ACL ID N12-1077
Title Measuring Word Relatedness Using Heterogeneous Vector Space Models
Venue Annual Conference of the North American Chapter of the Association for Computational Linguistics
Session Main Conference
Year 2012
Authors

Noticing that different information sources of- ten provide complementary coverage of word sense and meaning, we propose a simple and yet effective strategy for measuring lexical se- mantics. Our model consists of a committee of vector space models built on a text cor- pus, Web search results and thesauruses, and measures the semantic word relatedness us- ing the averaged cosine similarity scores. De- spite its simplicity, our system correlates with human judgements better or similarly com- pared to existing methods on several bench- mark datasets, including WordSim353.