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A Supervised Learning Approach to Entity Search

  • Guoping Hu
  • Jingjing Liu
  • Hang Li
  • Yunbo Cao
  • Jian-Yun Nie
  • Jianfeng Gao
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4182)

Abstract

In this paper we address the problem of entity search. Expert search and time search are used as examples. In entity search, given a query and an entity type, a search system returns a ranked list of entities in the type (e.g., person name, time expression) relevant to the query. Ranking is a key issue in entity search. In the literature, only expert search was studied and the use of co-occurrence was proposed. In general, many features may be useful for ranking in entity search. We propose using a linear model to combine the uses of different features and employing a supervised learning approach in training of the model. Experimental results on several data sets indicate that our method significantly outperforms the baseline method based solely on co-occurrences.

Keywords

Information Retrieval Entity Search Expert Search Time Search Supervised Learning 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Guoping Hu
    • 1
  • Jingjing Liu
    • 2
  • Hang Li
    • 4
  • Yunbo Cao
    • 4
  • Jian-Yun Nie
    • 3
  • Jianfeng Gao
    • 4
  1. 1.iFly Speech LabUniversity of Science and Technology of ChinaHefeiChina
  2. 2.College of Information Science & TechnologyNankai UniversityTianjinChina
  3. 3.Département d’informatique et de recherche opérationnelleUniversité de Montréal 
  4. 4.Microsoft Research AsiaBeijingChina

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