Improving Toponym Extraction and Disambiguation Using Feedback Loop

  • Mena B. Habib
  • Maurice van Keulen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7387)


This paper addresses two problems with toponym extraction and disambiguation. First, almost no existing works examine the extraction and disambiguation interdependency. Second, existing disambiguation techniques mostly take as input extracted toponyms without considering the uncertainty and imperfection of the extraction process.

It is the aim of this paper to investigate both avenues and to show that explicit handling of the uncertainty of annotation has much potential for making both extraction and disambiguation more robust.


Hide Markov Model Conditional Random Field Property Description Extraction Model Entity Extraction 
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.


  1. 1.
    Habib, M.B., van Keulen, M.: Named entity extraction and disambiguation: The reinforcement effect. In: Proc. of MUD 2011, Seatle, USA, pp. 9–16 (2011)Google Scholar
  2. 2.
    Ekbal, A., Bandyopadhyay, S.: A Hidden Markov Model Based Named Entity Recognition System: Bengali and Hindi as Case Studies. In: Ghosh, A., De, R.K., Pal, S.K. (eds.) PReMI 2007. LNCS, vol. 4815, pp. 545–552. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  3. 3.
    Wallach, H.: Conditional random fields: An introduction. Technical Report MS-CIS-04-21, University of Pennsylvania (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Mena B. Habib
    • 1
  • Maurice van Keulen
    • 1
  1. 1.Faculty of EEMCSUniversity of TwenteEnschedeThe Netherlands

Personalised recommendations