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SuperSense Tagging with a Maximum Entropy Markov Model

  • Giuseppe Attardi
  • Luca Baronti
  • Stefano Dei Rossi
  • Maria Simi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7689)

Abstract

We tackled the task of SuperSense tagging by means of the Tanl Tagger, a generic, flexible and customizable sequence labeler, developed as part of the Tanl linguistic pipeline. The tagger can be configured to use different classifiers and to extract features according to feature templates expressed through patterns, so that it can be adapted to different tagging tasks, including PoS and Named Entity tagging. The tagger operates in a Markov chain, using a statistical classifier to infer state transitions and dynamic programming to select the best overall sequence of tags. We exploited the extensive customization capabilities of the tagger in order to tune it for the task of SuperSense tagging, by performing an extensive process of feature selection. The resulting configuration achieved the best scores in the closed subtask.

Keywords

SuperSense Tagging Word Net Maximum Entropy Maximum Entropy Markov Model MEMM dynamic programming 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Giuseppe Attardi
    • 1
  • Luca Baronti
    • 1
  • Stefano Dei Rossi
    • 1
  • Maria Simi
    • 1
  1. 1.Dipartimento di InformaticaUniversità di PisaPisaItaly

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