Advertisement

Exploring Adaptive Window Sizes for Entity Retrieval

  • Fawaz Alarfaj
  • Udo Kruschwitz
  • Chris Fox
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8416)

Abstract

With the continuous attention of modern search engines to retrieve entities and not just documents for any given query, we introduce a new method for enhancing the entity-ranking task. An entity-ranking task is concerned with retrieving a ranked list of entities as a response to a specific query. Some successful models used the idea of association discovery in a window of text, rather than in the whole document. However, these studies considered only fixed window sizes. This work proposes a way of generating an adaptive window size for each document by utilising some of the document features. These features include document length, average sentence length, number of entities in the document, and the readability index. Experimental results show a positive effect once taking these document features into consideration when determining window size.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Alarfaj, F., Kruschwitz, U., Fox, C.: An adaptive window-size approach for expert-finding. In: DIR 2013, Delft, The Netherlands (April 2013)Google Scholar
  2. 2.
    Balog, K., Fang, Y., de Rijke, M., Serdyukov, P., Si, L.: Expertise retrieval. Foundations and Trends in Information Retrieval 6(2-3), 127–256 (2012)CrossRefGoogle Scholar
  3. 3.
    Balog, K., Azzopardi, L., de Rijke, M.: A language modeling framework for expert finding. Information Processing and Management 45(1), 1–19 (2009)CrossRefGoogle Scholar
  4. 4.
    Macdonald, C., Ounis, I.: Searching for expertise: Experiments with the voting model. The Computer Journal 52(7), 729–748 (2009)CrossRefGoogle Scholar
  5. 5.
    Miao, J., Huang, J.X., Ye, Z.: Proximity-based rocchio’s model for pseudo relevance. In: SIGIR 2012, Portland, Oregon, pp. 535–544 (2012)Google Scholar
  6. 6.
    Petkova, D., Croft, W.: Proximity-based document representation for named entity retrieval. In: CIKM 2007, pp. 731–740. ACM, New York (2007)Google Scholar
  7. 7.
    Zhu, J., Song, D., Rüger, S.: Integrating multiple windows and document features for expert finding. JASIST 60(4), 694–715 (2009)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Fawaz Alarfaj
    • 1
  • Udo Kruschwitz
    • 1
  • Chris Fox
    • 1
  1. 1.School of Computer Science and Electronic EngineeringUniversity of Essex ColchesterUK

Personalised recommendations