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Title:
Learning to detect task boundaries of query session
Author: Zhang, Zhenzhong (1) ; Sun, Le (1) ; Han, Xianpei (1)
Conference Name: 22nd ACM International Conference on Information and Knowledge Management, CIKM 2013
Conference Date: October 27, 2013 - November 1, 2013
Issued Date: 2013
Conference Place: San Francisco, CA, United states
Publish Place: Association for Computing Machinery, General Post Office, P.O. Box 30777, NY 10087-0777, United States
Indexed Type: EI
ISBN: 9781450322638
Department: (1) Institute of Software, Chinese Academy of Sciences, Beijing, China
Abstract: To accomplish a search task and satisfy a single information need, users usually submit a series of queries to web search engines. It is useful for web search engines to detect the task boundaries in a series of successive queries. Traditional task boundary detection methods are based on time gap and lexical comparisons, which often suffer from the vocabulary gap problem, that is, the topically related queries may not share any common words. In this paper we learn hidden topics from query log and leverage them to resolve the vocabulary gap problem. Unlike other external knowledge resources, such as WordNet and Wikipedia, the hidden topics discovered from query log cover long tail queries, which is useful to detect task boundaries. Experimental results on dataset from real world query log demonstrate that the proposed method achieves significant quality enhancement. Copyright © 2013 ACM.
English Abstract: To accomplish a search task and satisfy a single information need, users usually submit a series of queries to web search engines. It is useful for web search engines to detect the task boundaries in a series of successive queries. Traditional task boundary detection methods are based on time gap and lexical comparisons, which often suffer from the vocabulary gap problem, that is, the topically related queries may not share any common words. In this paper we learn hidden topics from query log and leverage them to resolve the vocabulary gap problem. Unlike other external knowledge resources, such as WordNet and Wikipedia, the hidden topics discovered from query log cover long tail queries, which is useful to detect task boundaries. Experimental results on dataset from real world query log demonstrate that the proposed method achieves significant quality enhancement. Copyright © 2013 ACM.
Language: 英语
Content Type: 会议论文
URI: http://ir.iscas.ac.cn/handle/311060/16649
Appears in Collections:软件所图书馆_会议论文

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Zhang, Zhenzhong ,Sun, Le ,Han, Xianpei . Learning to detect task boundaries of query session[C]. 见:22nd ACM International Conference on Information and Knowledge Management, CIKM 2013. San Francisco, CA, United states. October 27, 2013 - November 1, 2013.
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