Query Term Weighting based on Query Performance Prediction

TL;DR

This paper introduces a query term weighting method based on query performance prediction, which assesses how individual terms influence retrieval effectiveness by analyzing query variants, enhancing search re-ranking performance.

Contribution

It proposes a novel term weighting approach leveraging QPP to improve search re-ranking, validated across multiple state-of-the-art QPP methods and TREC datasets.

Findings

Effective term weighting improves search re-ranking accuracy

QPP-based weighting outperforms traditional methods

Validated on multiple TREC corpora

Abstract

This work presents a general query term weighting approach based on query performance prediction (QPP). To this end, a given term is weighed according to its predicted effect on query performance. Such an effect is assumed to be manifested in the responses made by the underlying retrieval method for the original query and its (simple) variants in the form of a single-term expanded query. Focusing on search re-ranking as the underlying application, the effectiveness of the proposed term weighting approach is demonstrated using several state-of-the-art QPP methods evaluated over TREC corpora.