A context‐dependent relevance model
Edward Kai Fung Dang, Robert W. P. Luk, James W Allan · Journal of the Association for Information Science and Technology · 2015
Numerous past studies have demonstrated the effectiveness of therelevance model(RM) for information retrieval (IR). This approach enables relevance or pseudo‐relevance feedback to be incorporated within the language modeling framework ofIR. In the traditionalRM, the feedback information is used to improve the estimate of thequerylanguage model. In this article, we introduce an extension ofRMin the setting of relevance feedback. Our method provides an additional way to incorporate feedback via the improvement of thedocumentlanguage models. Specifically, we make use of the context information of known relevant and nonrelevant documents to obtain weighted counts of query terms for estimating the document language models. The context information is based on the words (unigrams or bigrams) appearing within a text window centered on query terms. Experiments on several Text REtrieval Conference (TREC)collections show that our context‐dependent relevance model can improve retrieval performance over the baselineRM. Together with previous studies within theBM25 framework, our current study demonstrates that the effectiveness of our method for using context information inIRis quite general and not limited to any specific retrieval model.