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Related Experiment Videos

Parsing with probabilistic strictly locally testable tree languages.

Jose Luis Verdú-Mas1, Rafael C Carrasco, Jorge Calera-Rubio

  • 1Departament de Llenguatges i Sistemes Informàtics, Universidad de Alicante, E-03071 Alicante, Spain. verdu@dlsi.ua.es

IEEE Transactions on Pattern Analysis and Machine Intelligence
|July 15, 2005
PubMed
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This study introduces stochastic k-testable tree languages, which approximate rational tree languages. These models offer effective pattern classification and smoothing for natural language processing tasks.

Area of Science:

  • Computational linguistics
  • Formal language theory
  • Machine learning

Background:

  • Probabilistic k-gram models are effective for string pattern classification and handle unseen events with smoothing.
  • Stochastic rational tree languages are complex and require robust modeling techniques.

Purpose of the Study:

  • Introduce stochastic k-testable tree languages as a novel model.
  • Demonstrate their ability to approximate stochastic rational tree languages.
  • Apply these models to learn probabilistic structures from natural language data.

Main Methods:

  • Definition and formalization of stochastic k-testable tree languages.
  • Development of approximation algorithms for rational tree languages.
  • Implementation of a parser for learning probabilistic k-testable models from parsed sentences.

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Main Results:

  • Stochastic k-testable tree languages can effectively approximate any stochastic rational tree language.
  • The proposed model facilitates learning from parsed sentence samples.
  • A practical parser incorporating smoothing for natural language grammars is presented.

Conclusions:

  • Stochastic k-testable tree languages provide a powerful framework for modeling probabilistic structures in trees.
  • This approach enhances pattern classification and smoothing in natural language processing.
  • The developed methods offer a viable solution for learning from linguistic data.