Recently, the focus of many novel search applications has shifted from short keyword queries to verbose natural language queries. Examples include question answering systems and dialogue systems, voice search on mobile devices and entity search engines like Facebook’s Graph Search or Google’s Knowledge Graph. However the performance of textbook information retrieval techniques for such verbose queries is not as good as that for their shorter counterparts. Thus, effective handling of verbose queries has become a critical factor for adoption of information retrieval techniques in this new breed of search applications. Over the past decade, the information retrieval community has deeply explored the problem of transforming natural language verbose queries using operations like reduction, weighting, expansion, reformulation and segmentation into more effective structural representations. However, thus far, there was not a coherent and organized survey on this topic. In this survey, we aim to put together various research pieces of the puzzle, provide a comprehensive and structured overview of various proposed methods, and also list various application scenarios where effective verbose query processing can make a significant difference.

最近，许多新颖的搜索应用程序的重点已经从短关键字查询转变为冗长的自然语言查询。示例包括问题解答系统和对话系统，移动设备上的语音搜索以及诸如Facebook的Graph Search或Google的Knowledge Graph之类的实体搜索引擎。但是，针对此类冗长查询的教科书信息检索技术的性能不如其较短查询的性能好。因此，在这种新型搜索应用程序中，对详细查询的有效处理已成为采用信息检索技术的关键因素。在过去的十年中，信息检索社区深入探讨了使用归约，加权，扩展，重新制定和细分成更有效的结构表示。但是，到目前为止，还没有关于该主题的连贯和有组织的调查。在本次调查中，我们旨在汇总该难题的各个研究部分，提供各种建议方法的全面而结构化的概述，并列出有效的详细查询处理可以产生重大影响的各种应用场景。