PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Parsing with probabilistic strictly locally testable tree languages
José Luis Verdú-Mas, Rafael C. Carrasco and Jorge Calera-Rubio
IEEE Pattern Analysis and Machine Intelligence Volume accepted, to appear, 2004.


Probabilistic $k$-testable models (usually known as $k$-gram models in the case of strings) can be easily identified from samples and allow for smoothing techniques to deal with unseen events during pattern classification. In this paper, we introduce the family of stochastic $k$-testable tree languages and describe how these models can approximate any stochastic rational tree language. The model is applied to the task of learning a probabilistic $k$-testable model from a sample of parsed sentences. In particular, a parser for a natural language grammar that incorporates smoothing is shown.

PDF - Requires Adobe Acrobat Reader or other PDF viewer.
EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
Natural Language Processing
ID Code:751
Deposited By:Jorge Calera-Rubio
Deposited On:30 December 2004