Towards natural question guided search

Web search is generally motivated by an information need. Since asking well-formulated questions is the fastest and the most natural way to obtain information for human beings, almost all the queries posed to search engines correspond to some underlying questions, which represent the information need. Accurate determination of these questions may substantially improve the quality of search results and usability of search interfaces. In this paper, we propose a new framework for question-guided search, in which a retrieval system would automatically generate potentially interesting questions to the users. Since the answers to such questions are known to exist in search results, these questions can potentially guide the users directly to the answers they are looking for, eliminating the need to scan the documents in the results list. Moreover, in case of imprecise or ambiguous queries, automaticallygenerated questions can naturally engage the users into feedback cycles to refine their information need and guide them towards their search goals. Implementation of the proposed strategy raises new challenges in content indexing, question generation, ranking and feedback. We proposed new methods to address these challenges and evaluated them with a prototype system on a subset of Wikipedia. The evaluation results show the promise of this new question-guided search strategy. Categories andSubjectDescriptors