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Doctoral Thesis
DOI
10.11606/T.45.2010.tde-05082010-223515
Document
Author
Full name
Fábio Natanael Kepler
E-mail
Institute/School/College
Knowledge Area
Date of Defense
Published
São Paulo, 2010
Supervisor
Committee
Finger, Marcelo (President)
Silva, Flavio Soares Correa da
Aluisio, Sandra Maria
Milidiu, Ruy Luiz
Sousa, Maria Clara Paixao de
Title in Portuguese
Modelagem de contextos para aprendizado automático aplicado à análise morfossintática
Keywords in Portuguese
Cadeias de markov
Etiquetagem morfossintática
Linguística computacional
Abstract in Portuguese
A etiquetagem morfossintática envolve atribuir às palavras de uma sentença suas classes morfossintáticas de acordo com os contextos em que elas aparecem. Cadeias de Markov de Tamanho Variável (VLMCs, do inglês "Variable-Length Markov Chains") oferecem uma forma de modelar contextos maiores que trigramas sem sofrer demais com a esparsidade de dados e a complexidade do espaço de estados. Mesmo assim, duas palavras do português apresentam um alto grau de ambiguidade: 'que' e 'a'. O número de erros na etiquetagem dessas palavras corresponde a um quarto do total de erros cometidos por um etiquetador baseado em VLMCs. Além disso, essas palavras parecem apresentar dois diferentes tipos de ambiguidade: um dependendo de contexto não local e outro de contexto direito. Exploramos maneiras de expandir o modelo baseado em VLMCs através do uso de diferentes modelos e métodos, a fim de atacar esses problemas. As abordagens mostraram variado grau de sucesso, com um método em particular (aprendizado guiado) se mostrando capaz de resolver boa parte da ambiguidade de 'a'. Discutimos razões para isso acontecer. Com relação a 'que', ao longo desta tese propusemos e testamos diversos métodos de aprendizado de informação contextual para tentar desambiguá-lo. Mostramos como, em todos eles, o nível de ambiguidade de 'que' permanece praticamente constante.
Title in English
Modeling contexts for automatic learning applied to morphosyntactic analysis
Keywords in English
Computational linguistics
Markov chains
Part-of-speech tagging
Abstract in English
Part-of-speech tagging involves assigning to words in a sentence their part-of-speech class based on the contexts they appear in. Variable-Length Markov Chains (VLMCs) offer a way of modeling contexts longer than trigrams without suffering too much from data sparsity and state space complexity. Even so, two words in Portuguese show a high degree of ambiguity: 'que' and 'a'. The number of errors tagging these words corresponds to a quarter of the total errors made by a VLMC-based tagger. Moreover, these words seem to show two different types of ambiguity: one depending on non-local context and one on right context. We searched ways of expanding the VLMC-based model with a number of different models and methods in order to tackle these issues. The approaches showed variable degrees of success, with one particular method (Guided Learning) solving much of the ambiguity of 'a'. We explore reasons why this happened. Rega rding 'que', throughout this thesis we propose and test various methods for learning contextual information in order to try to disambiguate it. We show how, in all of them, the level of ambiguity shown by 'que' remains practically c onstant.
 
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Kepler2010PhDThesis.pdf (915.79 Kbytes)
Publishing Date
2011-05-12
 
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