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Probabilistic Models for Expert Finding

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Part of the Lecture Notes in Computer Science book series (LNISA,volume 4425)

Abstract

A common task in many applications is to find persons who are knowledgeable about a given topic (i.e., expert finding). In this paper, we propose and develop a general probabilistic framework for studying expert finding problem and derive two families of generative models (candidate generation models and topic generation models) from the framework. These models subsume most existing language models proposed for expert finding. We further propose several techniques to improve the estimation of the proposed models, including incorporating topic expansion, using a mixture model to model candidate mentions in the supporting documents, and defining an email count-based prior in the topic generation model. Our experiments show that the proposed estimation strategies are all effective to improve retrieval accuracy.

Keywords

  • Mixture Model
  • Query Model
  • Supporting Document
  • Expert Find
  • Probabilistic Ranking Principle

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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© 2007 Springer Berlin Heidelberg

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Fang, H., Zhai, C. (2007). Probabilistic Models for Expert Finding. In: Amati, G., Carpineto, C., Romano, G. (eds) Advances in Information Retrieval. ECIR 2007. Lecture Notes in Computer Science, vol 4425. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71496-5_38

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  • DOI: https://doi.org/10.1007/978-3-540-71496-5_38

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-71494-1

  • Online ISBN: 978-3-540-71496-5

  • eBook Packages: Computer ScienceComputer Science (R0)