Discriminative models of integrating document evidence and document-candidate associations for expert search

SIGIR 2010 Proceedings - 33rd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval

Volumn , Issue , 2010, Pages 683-690

  Fang, Yi ^a   Si, Luo ^a   Mathur, Aditya P ^a  

^a Purdue University   (United States)

Author keywords

Discriminative models; Enterprise search; Expert search

Indexed keywords

DISCRIMINATIVE LEARNING; DISCRIMINATIVE MODELS; ENTERPRISE SEARCHES; EXPERT SEARCHES; GENERATIVE MODEL; LANGUAGE MODEL; MACHINE LEARNING APPLICATIONS; STATISTICAL LANGUAGE MODELING;

COMPUTATIONAL LINGUISTICS; INDUSTRY; INFORMATION RETRIEVAL; RESEARCH;

NATURAL LANGUAGE PROCESSING SYSTEMS;

EID: 77956018747     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1835449.1835563     Document Type: Conference Paper

References (38)

1. P. Bailey, N. Craswell, A. De Vries, and I. Soboroff. Overview of the trec-2007 enterprise track. In TREC-15, 2007.
2. K. Balog. Non-local evidence for expert finding. In CIKM, 2008.
3. K. Balog, L. Azzopardi, and M. de Rijke. Formal models for expert finding in enterprise corpora. In SIGIR, 2006.
4. K. Balog, L. Azzopardi, and M. de Rijke. A language modeling framework for expert finding. Information Processing & Management, 45(1):1-19, 2009.
5. K. Balog and M. de Rijke. Finding experts and their details in e-mail corpora. In WWW, page 1036. ACM, 2006.
6. K. Balog and M. De Rijke. Associating people and documents. In ECIR, 2008.
7. K. Balog, I. Soboroff, P. Thomas, N. Craswell, A. de Vries, and P. Bailey. Overview of the trec-2008 enterprise track. In TREC-16, 2008.
8. Y. Cao, J. Liu, S. Bao, and H. Li. Research on expert search at enterprise track of TREC 2005. In TREC-13, 2005.
9. P. Carlile. Working knowledge: how organizations manage what they know. Human Resource Planning, 21(4):58-60, 1998.
10. H. Chen, H. Shen, J. Xiong, S. Tan, and X. Cheng. Social network structure behind the mailing lists: Ict-iiis at trec 2006 expert finding track. In TREC-14, 2006.
11. W. Cooper. Exploiting the maximum entropy principle to increase retrieval effectiveness. JASIST, 34(1):31-39.
12. N. Craswell, A. de Vries, and I. Soboroff. Overview of the trec-2005 enterprise track. In TREC-13, 2005.
13. J. Dennis and R. Schnabel. Numerical Methods for Unconstrained Optimization and Nonlinear Equations. Society for Industrial Mathematics, 1996.
14. H. Fang and C. Zhai. Probabilistic models for expert finding. In ECIR, 2007.
15. Y. Fang, L. Si, and A. Mathur. Ranking experts with discriminative probabilistic models. In SIGIR Workshop on Learning to Rank for Information Retrieval, 2009.
16. Y. Fu, W. Yu, Y. Li, Y. Liu, M. Zhang, and S. Ma. THUIR at TREC 2005: Enterprise track. In TREC-14, 2006.
17. N. Fuhr. Probabilistic models in information retrieval. The Computer Journal, 35(3):243, 1992.
18. T. Liu. Learning to rank for information retrieval. Foundations and Trends in Information Retrieval, 3(3):225-331, 2009.
19. T. Liu, J. Xu, T. Qin, W. Xiong, and H. Li. Letor: Benchmark dataset for research on learning to rank for information retrieval. In SIGIR Workshop on Learning to Rank for Information Retrieval, 2007.
20. C. Macdonald, D. Hannah, and I. Ounis. High quality expertise evidence for expert search. In ECIR, 2008.
21. C. Macdonald and I. Ounis. Voting for candidates: adapting data fusion techniques for an expert search task. In CIKM, 2006.
22. D. Metzler and W. Bruce Croft. Linear feature-based models for information retrieval. Information Retrieval, 10(3):257-274, 2007.
23. R. Nallapati. Discriminative models for information retrieval. In SIGIR, 2004.
24. A. Ng and M. Jordan. On discriminative vs. generative classifiers: a comparison of logistic regression and naive bayes. NIPS, 2002.
25. D. Petkova and W. Croft. Proximity-based document representation for named entity retrieval. In CIKM, 2007.
26. S. Robertson. The probability ranking principle in IR. Journal of documentation, 33(4):294-304, 1977.
27. S. Robertson and K. Jones. Relevance weighting of search terms. JASIST, 27(3):129-146, 1976.
28. S. Robertson, S. Walker, S. Jones, M. Hancock-Beaulieu, and M. Gatford. Okapi at TREC-4. In TREC-4, 1996.
29. P. Serdyukov and D. Hiemstra. Being omnipresent to be almighty: The importance of the global web evidence for organizational expert finding. In SIGIR Workshop on Future Challenges in Expertise Retrieval, 2008.
30. P. Serdyukov and D. Hiemstra. Modeling documents as mixtures of persons for expert finding. In ECIR, 2008.
31. P. Serdyukov, H. Rode, and D. Hiemstra. Modeling multi-step relevance propagation for expert finding. In CIKM, 2008.
32. I. Soboroff, A. de Vries, and N. Craswell. Overview of the trec-2006 enterprise track. In TREC-14, 2006.
33. T. Strohman, D. Metzler, H. Turtle, and W. Croft. Indri: A language model-based search engine for complex queries. In International Conference on Intelligence Analysis, 2004.
34. J. Tang, J. Zhang, L. Yao, J. Li, L. Zhang, and Z. Su. Arnetminer: Extraction and mining of academic social networks. In SIGKDD, 2008.
35. D. Tax, M. Van Breukelen, R. Duin, and J. Kittler. Combining multiple classifiers by averaging or by multiplying? Pattern recognition, 33(9):1475-1485, 2000.
36. D. Yimam-Seid and A. Kobsa. Expert finding systems for organizations. Sharing Expertise: Beyond Knowledge Management, 2003.
37. C. Zhai and J. Lafferty. A study of smoothing methods for language models applied to information retrieval. TOIS, 22(2):214, 2004.
38. J. Zhu, X. Huang, D. Song, and S. Ruger. Integrating multiple document features in language models for expert finding. Knowledge and Information Systems, pages 1-26.