Pseudo-relevance feedback query based on Wikipedia

Abstract

The traditional information retrieval (IR) model always only use the BOW (bag-of-words)-based retrieval model or Concepts-based retrieval model. However BOW-based model ignore the rich semantic relations between the words and text, and Concept-based model always bring in the noisy concepts and loss the precision. Pseudo-relevance feedback (PRF) is a widely used method for improving retrieval effectiveness, but it is strongly dependent on the precision of initial retrieval results. In order to solve these issues, this paper proposes a new concept generator called Enrichment-ESA which is the enrichment of the Explicit Semantic Analysis (ESA) method. With the help of Enrichment-ESA, we propose a novel PRF method which combined the BOW-based retrieval model and Concept-based retrieval model together to solve shortcomings of the existing IR model in some degree. The experimental results show that our method improves over the baseline method and performs better than the common PRF method.