Natural Language Processing for Expertise Modelling in E-mail Communication

  • Sanghee Kim
  • Wendy Hall
  • Andy Keane
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2412)


One way to find information that may be required, is to approach a person who is believed to possess it or to identify a person who knows where to look for it. Technical support, which automatically compiles individual expertise and makes this accessible, may be centred on an expert finder system. A central component of such a system is a user profile, which describes user expertise level in discussed subjects. Previous works have made attempts to weight user expertise by using content-based methods, which associate the expertise level with the analysis of keyword usage, irrespective of any semantic meanings conveyed. This paper explores the idea of using a natural language processing technique to understand given information from both a structural and semantic perspective in building user profiles. With its improved interpretation capability compared to prior works, it aims to enhance the performance accuracy in ranking the order of names of experts, returned by a system against a help-seeking query. To demonstrate its efficiency, e-mail communication is chosen as an application domain, since its closeness to a spoken dialog, makes it possible to focus on the linguistic attributes of user information in the process of expertise modelling. Experimental results from a case study show a 23% higher performance on average over 77% of the queries tested with the approach presented here.


Natural Language Processing User Profile User Expertise Expertise Modelling Expertise Level 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Allen, J. (1987) Natural Language Understanding, Benjamin/Cummings PublishingGoogle Scholar
  2. 2.
    Choo, C. W., Detlor, B., Turnbull, D. (2000) WEB WORK Information Seeking and Knowledge Work on the World Wide Web, Kluwer Academic PublishersGoogle Scholar
  3. 3.
    Forbes (2001) Forbes, You’ve got expertise,
  4. 4.
    Miller, G. A., Beckwith, R., Fellbaum, C., Gross, D., Miller, K. (1993) Introduction to WordNet: An On-Line Lexical Database, University of Princeton, U.S.A.Google Scholar
  5. 5.
    Sekine, S., Grishman, R. (1995) A Corpus-based Probabilistic Grammar with only Two Non-Terminals, In Proceedings of the Fourth International Workshop on Parsing Technology, pp. 216–223Google Scholar
  6. 6.
    Tacit (1999) White paper of KnowledgeMail,
  7. 7.
    Verschueren, J. (1980) On Speech Act Verbs, John Benjamins, AmsterdamGoogle Scholar
  8. 8.
    Vivacqua, A., Lieberman, H. (2000) Agents to Assist in Finding Help, In Proceedings of the CHI’2000, pp. 65–72Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Sanghee Kim
    • 1
  • Wendy Hall
    • 1
  • Andy Keane
    • 2
  1. 1.Intelligence, Agents, Multimedia Group, Department of Electronics and Computer ScienceUniversity of SouthamptonUK
  2. 2.Computational Engineering and Design Center, School of Engineering ScienceUniversity of SouthamptonUK

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