Relation Based Term Weighting Regularization

Traditional retrieval models compute term weights based on only the information related to individual terms such as TF and IDF. However, query terms are related. Intuitively, these relations could provide useful information about the importance of a term in the context of other query terms. For example, query "perl tutorial" specifies that a user look for information relevant to both perl and tutorial. Thus, a document containing both terms should have higher relevance score than the ones with only one of them. However, if the IDF value of "tutorial" is much smaller than "perl", existing retrieval models may assign the document lower score than those containing multiple occurrences of "perl". It is clear that the importance of a term should be dependent on not only collection statistics but also the relations with other query terms. In this work, we study how to utilize semantic relations among query terms to regularize term weighting. Experiment results over TREC collections show that the proposed strategy is effective to improve the retrieval performance.