Combining WordNet and ConceptNet for Automatic Query Expansion: A Learning Approach.
Journal
Information Retrieval Technology, 4th Asia Infomation Retrieval Symposium, AIRS 2008, Harbin, China, January 15-18, 2008, Revised Selected Papers
Pages
213-224
Date Issued
2008
Author(s)
Abstract
We present a novel approach that transforms the weighting task to a typical coarse-grained classification problem, aiming to assign appropriate weights for candidate expansion terms, which are selected from WordNet and ConceptNet by performing spreading activation. This transformation benefits us to automatically combine various features. The experimental results show that our approach successfully combines WordNet and ConceptNet and improves retrieval performance. We also investigated the relationship between query difficulty and effectiveness of our approach. The results show that query expansion utilizing the two resources obtains the largest improving effect upon queries of "medium" difficulty. © 2008 Springer-Verlag Berlin Heidelberg.
Type
conference paper