Krisztian Balog
Toine Bogers
Leif Azzopardi
Maarten de Rijke
Antal van den Bosch
ABSTRACT
Expertise retrieval has been largely unexplored on data other than the W3C collection. At the same time, many intranets of universities and other knowledge-intensive organisations offer examples of relatively small but clean multilingual expertise data, covering broad ranges of expertise areas. We first present two main expertise retrieval tasks, along with a set of baseline approaches based on generative language modeling, aimed at finding expertise relations between topics and people. For our experimental evaluation, we introduce (and release) a new test set based on a crawl of a university site. Using this test set, we conduct two series of experiments. The first is aimed at determining the effectiveness of baseline expertise retrieval methods applied to the new test set. The second is aimed at assessing refined models that exploit characteristic features of the new test set, such as the organizational structure of the university, and the hierarchical structure of the topics in the test set. Expertise retrieval models are shown to be robust with respect to environments smaller than the W3C collection, and current techniques appear to be generalizable to other settings.
- L. Azzopardi. Incorporating Context in the Language Modeling Framework for ad hoc Information Retrieval. PhD thesis, University of Paisley, 2005.Google Scholar
- K. Balog and M. de Rijke. Finding similar experts. In This volume, 2007.Google Scholar
- K. Balog and M. de Rijke. Determining expert profiles (with an application to expert finding). In IJCAI '07: Proc. 20th Intern. Joint Conf. on Artificial Intelligence, pages 2657--2662, 2007. Google ScholarDigital Library
- K. Balog, L. Azzopardi, and M. de Rijke. Formal models for expert finding in enterprise corpora. In SIGIR '06: Proc. 29th annual intern. ACM SIGIR conf. on Research and development in information retrieval, pages 43--50, 2006. Google ScholarDigital Library
- I. Becerra-Fernandez. The role of artificial intelligence technologies in the implementation of people-finder knowledge management systems. In AAAI Workshop on Bringing Knowledge to Business Processes, March 2000.Google ScholarDigital Library
- C. S. Campbell, P. P. Maglio, A. Cozzi, and B. Dom. Expertise identification using email communications. In CIKM '03: Proc. twelfth intern. conf. on Information and knowledge management, pages 528--531, 2003. Google ScholarDigital Library
- G. Cao, J.-Y. Nie, and J. Bai. Integrating word relationships into language models. In SIGIR '05: Proc. 28th annual intern. ACM SIGIR conf. on Research and development in information retrieval, pages 298--305, 2005. Google ScholarDigital Library
- T. M. Cover and J. A. Thomas. Elements of Information Theory. Wiley-Interscience, 1991. Google ScholarDigital Library
- N. Craswell, D. Hawking, A. M. Vercoustre, and P.Wilkins. P@noptic expert: Searching for experts not just for documents. In Ausweb, 2001.Google Scholar
- N. Craswell, A. de Vries, and I. Soboroff. Overview of the TREC-2005 Enterprise Track. In The Fourteenth Text REtrieval Conf. Proc. (TREC 2005), 2006.Google Scholar
- T. H. Davenport and L. Prusak. Working Knowledge: How Organizations Manage What They Know. Harvard Business School Press, Boston, MA, 1998. Google ScholarDigital Library
- T. Dunning. Accurate methods for the statistics of surprise and coincidence. Computational Linguistics, 19(1):61--74, 1993. Google ScholarDigital Library
- E. Filatova and J. Prager. Tell me what you do and I'll tell you what you are: Learning occupation-related activities for biographies. In HLT/EMNLP, 2005. Google ScholarDigital Library
- V. Lavrenko and W. B. Croft. Relevance based language models. In SIGIR '01: Proc. 24th annual intern. ACM SIGIR conf. on Research and development in information retrieval, pages 120--127, 2001. Google ScholarDigital Library
- V. Lavrenko, M. Choquette, and W. B. Croft. Cross-lingual relevance models. In SIGIR '02: Proc. 25th annual intern. ACM SIGIR conf. on Research and development in information retrieval, pages 175--182, 2002. Google ScholarDigital Library
- C. Macdonald and I. Ounis. Voting for candidates: adapting data fusion techniques for an expert search task. In CIKM '06: Proc. 15th ACM intern. conf. on Information and knowledge management, pages 387--396, 2006. Google ScholarDigital Library
- C. Manning and H. Schütze. Foundations of Statistical Natural Language Processing. The MIT Press, 1999. Google ScholarDigital Library
- A. Mockus and J. D. Herbsleb. Expertise browser: a quantitative approach to identifying expertise. In ICSE '02: Proc. 24th Intern. Conf. on Software Engineering, pages 503--512, 2002. Google ScholarDigital Library
- D. Petkova and W. B. Croft. Hierarchical language models for expert finding in enterprise corpora. In Proc. ICTAI 2006, pages 599--608, 2006. Google ScholarDigital Library
- I. Soboroff, A. de Vries, and N. Craswell. Overview of the TREC 2006 Enterprise Track. In TREC 2006 Working Notes, 2006.Google Scholar
- T. Tao, X. Wang, Q. Mei, and C. Zhai. Language model information retrieval with document expansion. In HLT-NAACL 2006, 2006. Google ScholarDigital Library
- TREC. Enterprise track, 2005. URL: http://www.ins.cwi.nl/projects/trec-ent/wiki/.Google Scholar
- G. van Noord. TextCat Language Guesser. URL: http://www.let.rug.nl/~vannoord/TextCat/.Google Scholar
- W3C. The W3C test collection, 2005. URL: http://research.microsoft.com/users/nickcr/w3c-summary.html.Google Scholar
Index Terms
- Broad expertise retrieval in sparse data environments
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