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Using symbol clustering to improve probabilistic automaton inference

  • Pierre Dupont
  • Lin Chase
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1433)

Abstract

In this paper we show that clustering alphabet symbols before PDFA inference is performed reduces perplexity on new data. This result is especially important in real tasks, such as spoken language interfaces, in which data sparseness is a significant issue. We describe the application of the ALERGIA algorithm combined with an independent clustering technique to the Air Travel Information System (ATIS) task. A 25 % reduction in perplexity was obtained. This result outperforms a trigram model under the same simple smoothing scheme.

Keywords

Deterministic Finite Automaton Alphabet Symbol Probabilistic Automaton Grammatical Inference Prefix Tree 
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 1998

Authors and Affiliations

  • Pierre Dupont
    • 1
  • Lin Chase
    • 2
  1. 1.EURISEUniversité Jean MonnetSaint-Etienne CedexFrance
  2. 2.LIMSI/CNRSOrsay CedexFrance

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