CrowdQ: Crowdsourced Query Understanding
CrowdQ: Crowdsourced Query Understanding
复制标题
CrowdQ：众包查询理解
DOI：
--
复制
发表时间：
2013
期刊：
影响因子：
--
通讯作者：
M. Franklin
中科院分区：
文献类型：
--
作者：
Gianluca Demartini;Beth Trushkowsky;Tim Kraska;M. Franklin
到目前为止，人机混合查询处理的工作主要集中在数据上：收集、清理和排序。在这篇文章中，我们解决了一个错失的使用众包来理解查询本身的机会。我们提出了一种新颖的人机混合方法，该方法利用群体来获取查询结构和实体关系的知识。该系统利用查询日志挖掘、自然语言处理(NLP)和众包的组合来生成查询模板，这些查询模板可以用于回答整个类别的不同问题，而不是只关注特定的问题和答案。
Work in hybrid human-machine query processing has thus far focused on the data: gathering, cleaning, and sorting. In this paper, we address a missed opportunity to use crowdsourcing to understand the query itself. We propose a novel hybrid human-machine approach that leverages the crowd to gain knowledge of query structure and entity relationships. The proposed system exploits a combination of query log mining, natural language processing (NLP), and crowdsourcing to generate query templates that can be used to answer whole classes of different questions rather than focusing on just a specific question and answer.