CrowdQ: Crowdsourced Query Understanding

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Provided by: Creative Commons
Topic: Big Data
Format: PDF
Work in hybrid human-machine query processing has thus far focused on the data: gathering, cleaning, and sorting. In this paper, the authors address a missed opportunity to use crowd-sourcing to understand the query itself. They 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 crowd-sourcing 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.
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