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New Technology of Library and Information Service  2014, Vol. 30 Issue (1): 36-42    DOI: 10.11925/infotech.1003-3513.2014.01.06
KNOWLEDGE ORGANIZATION AND KNOWLEDGE MANAGEMENT Current Issue | Archive | Adv Search |
A Hierarchical Framework for User Intention Recognition
Tang Jingxiao1, Lv Xueqiang1, Liu Chengyang2, Li Han2
1Beijing Key Laboratory of Internet Culture and Digital Dissemination Research,Beijing Information Science and Technology University,Beijing 100101,China; 2China National Institute of Standardization,Beijing 100191,China
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Abstract  [Objective] Any query search engine has its potential query intention,and accurate intention identification can improve the efficiency. [Methods] For the explicit intent queries,the authors employ sliding window strategy to find the maximum common substring for extracting user intent templates and then use the templates to identify the user intention. For implicit intent queries,the authors use a multi-feature integration method to build classifier for the final query intention recognition. [Results] Experimental results show that the hierarchical intention recognition framework can achieve better precision comparing with methods based on classifier,and the accuracy enhances 19.04%. [Limitations] Intention template obtaining is limited,so explicit intention recognition has limitation. For large-scale data,complexity of the pattern match and machine learning algorithm is very high,the algorithm need further optimization. [Conclusions] Experiment shows that this method is valid in Web intention recognition,which has a positive significance for improvement of intention recognition rate.
Key wordsInformation retrieval      Query intent      Hierarchical identification      Intent template     
Received: 14 February 2014      Published: 14 February 2014
:  TP391  

Cite this article:

Tang Jingxiao,Lv Xueqiang,Liu Chengyang,Li Han. A Hierarchical Framework for User Intention Recognition. New Technology of Library and Information Service, 2014, 30(1): 36-42.

URL:

http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.1003-3513.2014.01.06     OR     http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2014/V30/I1/36

[1] Broder A. A Taxonomy of Web Search[J]. ACM SIGIR Forum,2002,36(2):3-10.
[2]Jansen B J,Booth D L,Spink A. Determining the User Intent of Web Search Engine Queries[C]. In:Proceedings of the 16th International Conference on World Wide Web. New York,NY,USA:ACM,2007:1149-1150.
[3]Guo Q,Agichtein E. Exploring Mouse Movements for Inferring Query Intent[C]. In:Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. 2008:707-708.
[4]Herrera M R,Cristo M,Moura E S,et al. Exploring Features for the Automatic Identification of User Goals in Web Search[J]. Information Processing & Management,2010,46(2):131-142.
[5]Hu J,Wang G,Lochovsky F,et al. Understanding User’s Query Intent with Wikipedia[C]. In:Proceedings of the 18th International Conference on World Wide Web. New York,NY,USA:ACM,2009:471-480.
[6]张晓娟,陆伟. 利用查询重构识别查询意图[J]. 现代图书情报技术,2013(1):8-14. (Zhang Xiaojuan,Lu Wei. Identifying Query Intent by Exploiting Query Refinement[J]. New Technology of Library and Information Service,2013(1):8-14.)
[7]吴晓辉,宋萍萍,张荣欣. 有无查询意图的分类与实现架构模型研究[J]. 情报科学,2009,27(12):1829-1833. ( Wu Xiaohui,Song Pingping,Zhang Rongxin. Research on Implementation Framework Model and Classification Based on Query Intention and Non-query Intention[J]. Information Science,2009,27(12):1829-1833)
[8]袁鼎荣,钟宁,张师超. 文本信息处理研究述评[J]. 计算机科学,2011,38(2):9-13. ( Yuan Dingrong,Zhong Ning,Zhang Shichao. Research on Text Information Processing Review[J]. Computer Science,2011,38(2):9-13.)
[9]Li X,Wang Y Y,Acero A. Learning Query Intent from Regularized Click Graphs[C]. In:Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. New York,NY,USA:ACM,2008:339-346.
[10]王大玲,于戈,鲍玉斌,等. 基于用户搜索意图的Web网页动态泛化[J]. 软件学报,2010,21(5):1083-1097. (Wang Daling,Yu Ge,Bao Yubin,et al. Dynamically Generalizing Web Pages Based on Users’ Search Intentions[J]. Journal of Software,2010,21(5):1083-1097.)
[11]修驰,宋柔.基于无监督学习的专业领域分词歧义消解方法[J]. 计算机应用,2013,33(3):780-783. (Xiu Chi,Song Rou. Disambiguation of Domain Word Segmentation Based on Unsupervised Learning[J]. Journal of Computer Applications,2013,33(3):780-783.)
[12]伍大勇,赵世奇,刘挺,等. 融合多类特征识别Web搜索意图识别[J]. 模式识别与人工智能,2012,25(3):500-505. (Wu Dayong,Zhao Shiqi,Liu Ting,et al. Identification of Query Intents via Combining Multiple Features[J]. Pattern Recognition and Artificial Intelligence,2012,25(3):500- 505.)
[13]陈翀,刘晓兵,徐谷子,等.一种搜索引擎的查询意图发现的新方法[J]. 情报学报,2012,31(3):242-249. ( Chen Chong,Liu Xiaobing,Xu Guzi,et al. A New Method of Detecting Query Intent for Search Engines[J]. Journal of the China Society for Scientific and Technical Information,2012,31(3):242-249.)
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