Dynamic Bayesian Networks and Variable Length Genetic Algorithm for Dialogue Act Recognition

The recognition of dialogue act is a task of crucial importance for the processing of natural language in many applications such as dialogue system. However, it is one of the most challenging problems. The current dialogue act recognition models, namely cue-based models, are based on machine learnin...

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Main Author:	Ali Yahya, Anwar
Format:	Thesis
Language:	English English
Published:	2007
Subjects:	Machine learning. Genetic algorithms.
Online Access:	http://psasir.upm.edu.my/id/eprint/5217/1/FSKTM_2007_17.pdf
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id	my-upm-ir.5217
record_format	uketd_dc
spelling	my-upm-ir.52172013-05-27T07:21:14Z Dynamic Bayesian Networks and Variable Length Genetic Algorithm for Dialogue Act Recognition 2007 Ali Yahya, Anwar The recognition of dialogue act is a task of crucial importance for the processing of natural language in many applications such as dialogue system. However, it is one of the most challenging problems. The current dialogue act recognition models, namely cue-based models, are based on machine learning techniques, particularly statistical ones. Despite the success of the cue-based models, they still have serious drawbacks. Among them are, inadequate representation of dialogue context, intra-utterance and inter-utterances independencies assumptions, inaccurate estimation of the recognition accuracy and suboptimality of the lexical cues selection approaches. Motivating by these drawbacks, this research proposes a new model of dialogue act recognition in which dynamic Bayesian machine learning is applied to induce dynamic Bayesian networks models from task-oriented dialogue corpus using sets of lexical cues selected automatically by means of new variable length genetic algorithm. In achieving this, the research is planned in three main stages. In the initial stage, the dynamic Bayesian networks models are constructed based on a set of lexical cues selected tentatively from the dialogue corpus. The results are compared with the results of static Bayesian networks and naïve bayes. The results confirm the merits of using dynamic Bayesian networks for dialogue act recognition. In the second stage, the previous ranking approaches are investigated for the selection of lexical cues. The main drawbacks of these approaches are highlighted, and based on that an alternative approach is proposed. The proposed approach consists of preparation phase and selection phase. The preparation phase transforms the original dialogue corpus into phrases space. In the selection phase, a new variable length genetic algorithm is applied to select the lexical cues. The results of the proposed approach are compared with the results of the ranking approaches. The results provide experimental evidences on the ability of the proposed approach to avoid the drawbacks of the ranking approaches. In the final stage; the dynamic Bayesian networks models are redesigned using the lexical cues generated from the proposed lexical cues selection approaches. The results confirm the effectiveness of proposed approaches for the design of dialogue act recognition model. Machine learning. Genetic algorithms. 2007 Thesis http://psasir.upm.edu.my/id/eprint/5217/ http://psasir.upm.edu.my/id/eprint/5217/1/FSKTM_2007_17.pdf application/pdf en public phd doctoral Universiti Putra Malaysia Machine learning. Genetic algorithms. Computer Science and Information Technology English
institution	Universiti Putra Malaysia
collection	PSAS Institutional Repository
language	English English
topic	Machine learning. Genetic algorithms.
spellingShingle	Machine learning. Genetic algorithms. Ali Yahya, Anwar Dynamic Bayesian Networks and Variable Length Genetic Algorithm for Dialogue Act Recognition
description	The recognition of dialogue act is a task of crucial importance for the processing of natural language in many applications such as dialogue system. However, it is one of the most challenging problems. The current dialogue act recognition models, namely cue-based models, are based on machine learning techniques, particularly statistical ones. Despite the success of the cue-based models, they still have serious drawbacks. Among them are, inadequate representation of dialogue context, intra-utterance and inter-utterances independencies assumptions, inaccurate estimation of the recognition accuracy and suboptimality of the lexical cues selection approaches. Motivating by these drawbacks, this research proposes a new model of dialogue act recognition in which dynamic Bayesian machine learning is applied to induce dynamic Bayesian networks models from task-oriented dialogue corpus using sets of lexical cues selected automatically by means of new variable length genetic algorithm. In achieving this, the research is planned in three main stages. In the initial stage, the dynamic Bayesian networks models are constructed based on a set of lexical cues selected tentatively from the dialogue corpus. The results are compared with the results of static Bayesian networks and naïve bayes. The results confirm the merits of using dynamic Bayesian networks for dialogue act recognition. In the second stage, the previous ranking approaches are investigated for the selection of lexical cues. The main drawbacks of these approaches are highlighted, and based on that an alternative approach is proposed. The proposed approach consists of preparation phase and selection phase. The preparation phase transforms the original dialogue corpus into phrases space. In the selection phase, a new variable length genetic algorithm is applied to select the lexical cues. The results of the proposed approach are compared with the results of the ranking approaches. The results provide experimental evidences on the ability of the proposed approach to avoid the drawbacks of the ranking approaches. In the final stage; the dynamic Bayesian networks models are redesigned using the lexical cues generated from the proposed lexical cues selection approaches. The results confirm the effectiveness of proposed approaches for the design of dialogue act recognition model.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Ali Yahya, Anwar
author_facet	Ali Yahya, Anwar
author_sort	Ali Yahya, Anwar
title	Dynamic Bayesian Networks and Variable Length Genetic Algorithm for Dialogue Act Recognition
title_short	Dynamic Bayesian Networks and Variable Length Genetic Algorithm for Dialogue Act Recognition
title_full	Dynamic Bayesian Networks and Variable Length Genetic Algorithm for Dialogue Act Recognition
title_fullStr	Dynamic Bayesian Networks and Variable Length Genetic Algorithm for Dialogue Act Recognition
title_full_unstemmed	Dynamic Bayesian Networks and Variable Length Genetic Algorithm for Dialogue Act Recognition
title_sort	dynamic bayesian networks and variable length genetic algorithm for dialogue act recognition
granting_institution	Universiti Putra Malaysia
granting_department	Computer Science and Information Technology
publishDate	2007
url	http://psasir.upm.edu.my/id/eprint/5217/1/FSKTM_2007_17.pdf
_version_	1747810375848427520

Dynamic Bayesian Networks and Variable Length Genetic Algorithm for Dialogue Act Recognition

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