NDLI: Unsupervised identification of synonymous query intent templates for attribute intents

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Unsupervised identification of synonymous query intent templates for attribute intents

Content Provider	ACM Digital Library
Author	Hsu, Bo-June Paul Li, Yanen Zhai, ChengXiang
Abstract	Among all web search queries there is an important subset of queries containing entity mentions. In these queries, it is observed that users are most interested in requesting some attribute of an entity, such as "Obama age" for the intent of age, which we refer to as the attribute intent. In this work we address the problem of identifying synonymous query intent templates for the attribute intent. For example, "how old is [Person]" and "[Person]'s age" are both synonymous templates for the age intent. Successful identification of the synonymous query intent templates not only can improve the performance of all existing query annotation approaches, but also could benefit applications such as instant answers and intent-based query suggestion. In this work we propose a clustering framework with multiple kernel functions to identify synonymous query intent templates for a set of canonical templates jointly. Furthermore, signals from multiple sources of information are integrated into a kernel function between templates, where the weights of these signals are tuned in an unsupervised manner. We have conducted extensive experiments across multiple domains in FreeBase, and results demonstrate the effectiveness of our clustering framework for finding synonymous query intent templates for attribute intents.
Starting Page	2029
Ending Page	2038
Page Count	10
File Format	PDF
ISBN	9781450322638
DOI	10.1145/2505515.2505694
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2013-10-27
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Clustering with multiple kernels Attribute intent Synonymous query intent templates
Content Type	Text
Resource Type	Article

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