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2009
ACM

Extracting structured information from user queries with semi-supervised conditional random fields

9 years 4 months ago
Extracting structured information from user queries with semi-supervised conditional random fields
When search is against structured documents, it is beneficial to extract information from user queries in a format that is consistent with the backend data structure. As one step toward this goal, we study the problem of query tagging which is to assign each query term to a pre-defined category. Our problem could be approached by learning a conditional random field (CRF) model (or other statistical models) in a supervised fashion, but this would require substantial human-annotation effort. In this work, we focus on a semi-supervised learning method for CRFs that utilizes two data sources: (1) a small amount of manually-labeled queries, and (2) a large amount of queries in which some word tokens have derived labels, i.e., label information automatically obtained from additional resources. We present two principled ways of encoding derived label information in a CRF model. Such information is viewed as hard evidence in one setting and as soft evidence in the other. In addition to th...
Xiao Li, Ye-Yi Wang, Alex Acero
Added 28 May 2010
Updated 28 May 2010
Type Conference
Year 2009
Where SIGIR
Authors Xiao Li, Ye-Yi Wang, Alex Acero
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