  Pseudo relevance feedback (PRF) automatically performs query expansion based
on top-retrieved documents to better represent the user's information need so
as to improve the search results. Previous PRF methods mainly select expansion
terms with high occurrence frequency in top-retrieved documents or with high
semantic similarity with the original query. However, existing PRF methods
hardly try to understand the content of documents, which is very important in
performing effective query expansion to reveal the user's information need. In
this paper, we propose a QA-based framework for PRF called QA4PRF to utilize
contextual information in documents. In such a framework, we formulate PRF as a
QA task, where the query and each top-retrieved document play the roles of
question and context in the corresponding QA system, while the objective is to
find some proper terms to expand the original query by utilizing contextual
information, which are similar answers in QA task. Besides, an attention-based
pointer network is built on understanding the content of top-retrieved
documents and selecting the terms to represent the original query better. We
also show that incorporating the traditional supervised learning methods, such
as LambdaRank, to integrate PRF information will further improve the
performance of QA4PRF. Extensive experiments on three real-world datasets
demonstrate that QA4PRF significantly outperforms the state-of-the-art methods.
