微信服务号

微信订阅号

2025-4-16- 17
Home > Archive>Volume 24, Issue 11, 2013 >2734-2746. DOI:10.3724/SP.J.1001.2013.04480
PDF HTML XML Export Cite reminder
Undirected Graph Model for Expert Evidence Document Recognition
Author:
Affiliation:

  • Article
    • | |
  • Metrics
    • |
  • Reference [21]
    • |
  • Related [20]
    • |
  • Cited by
    • | |
  • Comments
    Abstract:

    Expert evidence document recognition is the key step for expert search. Combining specialist candidate document independent page features and correlation among pages, this paper proposes an expert evidence document recognition method based on undirected graph model. First, independent page features such as words, URL links and expert metadata in all kinds of expert evidence document, and correlations such as links and content among candidate expert evidence document are analyzed. Then, independent page features and correlation among pages are integrated into the undirected graph to construct an undirected graph model for expert evidence document recognition. Finally, feature weights are learned in the model by using the gradient descent method and expert evidence document recognition is achieved by utilizing Gibbs Sampling method. The effectiveness of the proposed method is verified by comparison experiment. The experimental results show that the proposed method has a better effect.

    Reference
    [1] Macdonald C, Ounis I. Voting for candidates: Adapting data fusion techniques for an expert search task. In: Proc. of the 15th ACM Int''l Conf. on Information and Knowledge Management. New York: ACM Press, 2006. 387-396.[doi: 10.1145/1183614.1183671]
    [2] Macdonald C, Hannah D, Ounis I. High quality expertise evidence for expert search. Lecture Notes in Computer Science, 2008, 4956:283-295.[doi: 10.1007/978-3-540-78646-7_27]
    [3] Craswell N, de Vries AP, Soboroff I. Overview of the trec-2005 enterprise track. In: Proc. of the TREC 2005 Conf. New York: IEEE Press, 2005. 199-205.
    [4] Xi WS, Fox EA, Tan RP, Shu J. Machine learning approach for homepage finding task. In: Proc. of the 9th Int''l Symp. on String Processing and Information Retrieval. Berlin, Heidelberg: Springer-Verlag, 2002. 145-159.[doi: 10.1007/3-540-45735-6_14]
    [5] Tang J, Zhang D, Yao LM. Social network extraction of academic researchers. In: Proc. of the 17th IEEE Int''l Conf. on Data Mining (ICDM 2007). Washington: IEEE Press, 2007. 292-301.[doi: 10.1109/ICDM.2007.30]
    [6] Bron M, Balog K, de Rijke M. Ranking related entities: Components and analyses. In: Proc. of the 19th ACM Int''l Conf. on Information and Knowledge Management. New York: ACM Press, 2010. 1079-1088.[doi: 10.1145/1871437.1871574]
    [7] Li LN, Yu ZT, Zou JJ, Su L, Xian YT, Mao CL. Research on entity homepage recognition method. Journal of Computational Information System, 2009,5(6):1617-1624.
    [8] Fang Y, Si L, Yu ZT, Xian YT, Xu YB. Entity retrieval with hierarchical relevance model. In: Proc. of the 18th Text REtrieval Conf. (TREC 2009). New York: IEEE Press, 2009.
    [9] Fang Y, Si L, Mathur AP. Discriminative graphical models for faculty homepage discovery. Journal of Information Retrieval, 2010, 13(6):618-635.[doi: 10.1007/s10791-010-9127-7]
    [10] Wu ZJ, Yu ZT, Su L, Liu L, Xian YT. Research on the method of expert homepage recognition based on Markov logic networks. Journal of Computational Information System, 2012,8(3):1089-1096.
    [11] Macdonald C, Ounis I. Voting for candidates: Adapting data fusion techniques for an expert search task. In: Proc. of the CIKM 2006. New York: ACM Press, 2006. 387-396.[doi: 10.1145/1183614.1183671]
    [12] Balog K, Azzopardi L, de Rijke M. Formal models for expert finding in enterprise corpora. In: Proc. of the SIGIR 2006. New York: ACM Press, 2006. 43-50.[doi: 10.1145/1148170.1148181]
    [13] Jordan MI. Graphical models. Statistical Science, 2004,19(1):140-155.[doi: 10.1214/088342304000000026]
    [14] Koller D, Friedman N. Probabilistic Graphical Models: Principles and Techniques. Cambridge: Massachusetts Institute of Technology Press, 2009.[doi: 10.1007/978-3-642-38466-0_28]
    [15] Tian W, Shen T, Yu ZT, Guo JY, Xian YT. A Chinese expert name disambiguation approach based on spectral clustering with the expert page-associated relationships. Lecture Notes in Electrical Engineering, 2013,256:2013. 245-253.
    [16] Ng AY, Jordan MI, Weiss Y. On spectral clustering: Analysis and an algorithm. In: Dietterich TG, Becker S, Ghahramani Z, eds. Advances in Neural Information Processing Systems (NIPS) 14. Cambridge: MIT Press, 2002. 894-856.
    [17] Wang L, Bo LF, Jiao LC. Density-Sensitive semi-supervised spectral clustering. Ruan Jian Xue Bao/Journal of Software, 2007, 18(10):2412-2422 (in Chinese with English abstract). http://www.jos.org.cn/1000-9825/18/2412.htm[doi: 10.1360/jos182412]
    [18] Wu ZJ, Yu ZT, Guo JY, Mao CL, Zhang YM. Fusion of long distance dependency features for Chinese named entity recognition based on Markov logic networks. In: Proc. of the Natural Language Processing and Chinese Computing. Natural Language Processing and Chinese Computing Communications in Computer and Information Science, 2012,333:132-142.[doi: 10.1007/978- 3-642-34456-5_13]
    [19] Luenberger DG. Optimization by Vector Space Methods. Hoboken: Wiley-Interscience, 1997.
    [20] Zhang D, Lee WS. Question classification using support vector machines. In: Proc. of the 26th Annual Int''l ACM SIGIR Conf. on Research and Development in Informaion Retrieval. New York: ACM Press, 2003. 26-32.[doi: 10.1145/860435.860443]
    [21] Aizawa A. An information-theoretic perspective of TF-IDF measures. Information Processing & Management, 2003,39(1):45-65.[doi: 10.1016/S0306-4573(02)00021-3]
    Cited by
Get Citation

毛存礼,余正涛,吴则建,郭剑毅,线岩团.专家证据文档识别无向图模型.软件学报,2013,24(11):2734-2746

Copy
Share
Article Metrics
  • Abstract:5665
  • PDF: 7269
  • HTML: 0
  • Cited by: 0
History
  • Received:May 06,2013
  • Revised:August 02,2013
  • Online: November 01,2013
Links:Institute of Software, CASChina Computer FederationCASNSFC
You are the first2035318Visitors
Copyright: Institute of Software, Chinese Academy of Sciences Beijing ICP No. 05046678-4
Address：4# South Fourth Street, Zhong Guan Cun, Beijing 100190,Postal Code：100190
Phone：010-62562563 Fax：010-62562533 Email：jos@iscas.ac.cn
Technical Support：Beijing Qinyun Technology Development Co., Ltd.

Beijing Public Network Security No. 11040202500063