Query Aspect Based Term Weighting Regularization in Information Retrieval

Traditional retrieval models assume that query terms are independent and rank documents primarily based on various term weighting strategies including TF-IDF and document length normalization. However, query terms are related, and groups of semantically related query terms may form query aspects. Intuitively, the relations among query terms could be utilized to identify hidden query aspects and promote the ranking of documents covering more query aspects. Despite its importance, the use of semantic relations among query terms for term weighting regularization has been under-explored in information retrieval. In this paper, we study the incorporation of query term relations into existing retrieval models and focus on addressing the challenge, i.e., how to regularize the weights of terms in different query aspects to improve retrieval performance. Specifically, we first develop a general strategy that can systematically integrate a term weighting regularization function into existing retrieval functions, and then propose two specific regularization functions based on the guidance provided by constraint analysis. Experiments on eight standard TREC data sets show that the proposed methods are effective to improve retrieval accuracy.