A Term Association Approach for Genomics Information Retrieval

Modeling and mining term association is important for information retrieval, which allows an information retrieval system to retrieve relevant documents/passages more precisely. In this paper, we propose a new approach for discovering term associations among the keywords from a query. First, factor analysis is applied to discover some hidden common factors as the "eliteness" variables that can be used to estimate the importance of term associations. Second, a factor analysis based model and a corresponding algorithm are proposed. Then, we report experimental results that confirm the effectiveness and superiority of the proposed term association approach. Our approach achieves excellent results on the TREC 2007 and 2006 data sets, which provides a promising avenue for constructing high performance information retrieval systems.