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Probabilistic k-Testable Tree Languages

  • Juan Ramón Rico-Juan
  • Jorge Calera-Rubio
  • Rafael C. Carrasco
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1891)

Abstract

In this paper, we present a natural generalization of k-gram models for tree stochastic languages based on the k-testable class. In this class of models, frequencies are estimated for a probabilistic regular tree grammar wich is bottom-up deterministic. One of the advantages of this approach is that the model can be updated in an incremental fashion. This method is an alternative to costly learning algorithms (as inside-outside-based methods) or algorithms that require larger samples (as many state merging/splitting methods).

Keywords

Markov Chain Model Tree Automaton Tree Language Stochastic Sample Tree Grammar 
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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Copyright information

© Springer-Verlag Berlin Heidelberg 2000

Authors and Affiliations

  • Juan Ramón Rico-Juan
    • 1
  • Jorge Calera-Rubio
    • 1
  • Rafael C. Carrasco
    • 1
  1. 1.Departament de Llenguatges i Sistemes InformàticsUniversitat d’AlacantAlacanSpain

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