Query Performance Prediction for Multifield Document Retrieval

Abstract

The goal of the query performance prediction (QPP) task is to estimate retrieval effectiveness in the absence of relevance judgments. We consider a novel task of predicting the performance of multifield document retrieval. In this setting, documents are assumed to consist of several different textual descriptions (fields) on which the query is being evaluated. Overall, we study three predictor types. The first type applies a given basic QPP method directly on the retrieval's outcome. Building on the idea of reference-lists, the second type utilizes several pseudo-effective (PE) reference-lists. Each such list is retrieved by further evaluating the query over a specific (single) document field. The third predictor is built on the assumption that, a high agreement among the single-field PE reference-lists attests to a more effective retrieval. Using three different multifield document retrieval tasks we demonstrate the merits of our extended QPP methods. Specifically, we show the important role that the intrinsic agreement among the single-field PE reference-lists plays in this extended QPP task.