[1]WANG Zhanyi,XU Weiran,GUO Jun.New technologies of intelligent text search[J].CAAI Transactions on Intelligent Systems,2012,7(1):40-49.
Copy

New technologies of intelligent text search

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 7 Number of periods: 2012 1 Page number: 40-49 Column: 学术论文—自然语言处理与理解 Public date: 2012-02-25
Title:
New technologies of intelligent text search
Author(s):
WANG Zhanyi1; 2;  XU Weiran1; 2;  GUO Jun1; 2
1. Pattern Recognition and Intelligent System (PRIS) Laboratory, Beijing University of Posts and Telecommunications, Beijing 100876, China;
2. School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China
Keywords:
intelligent text search;  text retrieval;  text analysis
CLC:
TP393
DOI:
-
Abstract:
To adapt to the massive amount of information on the internet and the need for accuracy, efficiency, and individualization, a set of technologies of intelligent text search including information retrieval, extraction, and filtering were proposed. First, new technologies of information retrieval were illustrated including the subtasks of enterprise retrieval, entity retrieval, blog retrieval, and relevance feedback. Second, the subtask of entity linking and slot filling related to information extraction was introduced. Finally, the subtask of spam email filtering related to information filtering was described. These technologies were converged for application in many wellknown international evaluations. These include the text retrieval conference (TREC) and text analysis conference (TAC) sponsored in the USA, and these technologies of intelligent text search were proven in practical applications such as public opinions on the Internet, short message opinions, and the campus object search engine (COSE).
References:
［1］郭军.Web搜索［M］.北京：高等教育出版社, 2009: 13.
［2］方慧.TREC发展历程及现状分析［J］.新世纪图书馆, 2010(1): 57.
?FANG Hui. On developing course and status analysis of TREC［J］. New Century Library, 2010(1): 57.
［3］BALOG K, SOBOROFF I, THOMAS P, et al. Overview of the TREC 2008 enterprise track［EB/OL］. ［20101215］. http://trec.nist.gov/pubs/trec17/papers/ENTERPRISE.OVERVIEW.pdf.
［4］RU Zhao, CHEN Yuehua, XU Weiran, et al. TREC2005 enterprise track experiments at BUPT［EB/OL］. ［20101215］. http://trec.nist.gov/pubs/trec14/papers/ beijinguofpt.ent.pdf.
［5］RU Zhao, LI Qian, XU Weiran, et al. BUPT at TREC 2006: enterprise track［EB/OL］. ［20101215］. http://trec.nist.gov/pubs/trec15/papers/beijingupt.ent.final.pdf.
［6］BAILEY P, CRASWELL N. Overview of the TREC 2007 enterprise track［EB/OL］. ［20101215］. http://trec. nist.gov/pubs/trec16/papers/ENT.OVERVIEW16.pdf.
［7］WANG Zhanyi, LIU Dongxin, XU Weiran, et al. BUPT at TREC 2009: entity track［EB/OL］. ［20101215］. http://trec.nist.gov/pubs/trec18/papers/bupt.ENT.pdf.
［8］ZHANG Suxiang, WEN Juan, WANG Xiaojie, et al. Automatic entity relation extraction based on maximum entropy［C］//Proceedings of the Sixth International Conference on Intelligent Systems Design and Applications. Ji’nan, China, 2006: 540544.
［9］LAFFERTY J D, MCCALLUM A, PEREIRA F C N. Conditional random fields: probabilistic models for segmenting and labeling sequence data［C］//Proceedings of the International Conference on Machine Learning. San Francisco, USA: Morgan Kaufmann Publishers Inc, 2001: 282289.
［10］MACDONALD C, OUNIS I. Voting for candidates: adapting data fusion techniques for an expert search task［C］//Proceedings of the 15th ACM International Conference on Information and Knowledge Management. New York, USA: ACM, 2006: 387396.
［11］MANNING C D, RAGHAVAN P, SCHUTZE H, An introduction to information retrieval［M］. Cambridge, UK: Cambridge University Press, 2008: 120126.
［12］WILSON T, WIEBE J, HOFFMANN P, Recognizing contextual polarity in phraselevel sentiment analysis［C］//Proceedings of the Conference on Human Language Technology and Empirical Methods in Natural Language Processing. Stroudsburg, USA: Association for Computational Linguistics, 2005: 347354.
［13］MANNING C D, SCHTZE H. Foundations of statistical natural language processing［M］. Cambridge, USA: The MIT Press, 1999.
［14］AMATI G. Probabilistic models for information retrieval based on divergence from randomness［D］. Glasgow, UK: University of Glasgow, 2003.
［15］SINGHAL A, BUCKLEY C, MITRA M. Pivoted document length normalization［C］//Proceedings of the 19th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. New York, USA: ACM, 1996: 2129.
［16］LI Si, LI Xinsheng. PRIS at 2009 relevance feedback track: experiments in language model for relevance feedback［EB/OL］. ［20101215］. http://trec.nist.gov/pubs/ trec18/papers/pris.RF.pdf.
［17］LALMAS M, MACFARLANE A, RUGER S. Advances in information retrieval［M］. New York, USA: SpringerVerlag, 2002: 74172.
［18］PONTE J M, CROFT W B. A language modeling approach to information retrieval［C］//Proceedings of the 21th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. New York, USA: ACM, 1998: 275281.
［19］WANG Bingqing, HUANG Xuanjing. Relevance feedback based on constrained clustering: FDU at TREC’09［EB/OL］. ［20101215］. http://trec.nist.gov/pubs/ trec18/papers/fudanu.RF.pdf.
［20］LAVRENKO V, CROFT W B. Relevancebased language models［C］//Proceedings of the 24th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. New York, USA: ACM, 2001: 120127.
［21］CHANG Chihchung, LIN Chihjen. LIBSVM: a library for support vector machines［EB/OL］. ［20110409］. http://www.csie.ntu.edu.tw/~cjlin/libsvm/index.html.
［22］The Lemur Project. INDRI: language modeling meets inference networks［EB/OL］. ［20110323］. http://www.lemurproject. org/indri/.
［23］TAC 2009. Knowledge base population track［EB/OL］. (20090929)［20101216］. http://apl.jhu.edu/~paulmac/kbp.html.
［24］TAC 2010. Knowledge base population (KBP2010) track［EB/OL］. (20100912)［20101216］. http://nlp.cs.qc.cuny.edu/kbp/2010/.
?［25］CRF++: yet another CRF toolkit［EB/OL］. ［201012 16］. http://crfpp.sourceforge.net/.
［26］YANG Zhen, XU Weiran, CHEN Bo, et al. PRIS Kidult antiSPAM solution at the TREC 2005 spam track: improving the performance of naive Bayes for spam detection［EB/OL］. ［20101215］. http://trec.nist.gov/pubs/trec14/papers/beijinguofpt.spam.pdf.
［27］YANG Zhen, XU Wei, CHEN Bo, et al. BUPT at TREC 2006: spam track［EB/OL］. ［20101215］. http://trec.nist.gov/pubs/trec15/papers/beijingupt.spam.final.pdf.
［28］CORMACK G V. TREC 2007 spam track overview［EB/OL］. ［20101215］. http://trec.nist.gov/pubs/trec16/papers/SPAM.OVERVIEW16.pdf.
?［29］杨震.文本分类和聚类中若干问题的研究［D］.北京：北京邮电大学, 2007: 1086.
?YANG Zhen. Research on key problems in text classification and clustering［D］. Beijing: Beijing University of Posts and Telecommunications, 2007: 1086.
Similar References:

Memo

-

Last Update: 2012-05-07

Copyright © CAAI Transactions on Intelligent Systems