Dynamic Bayesian networks and variable length genetic algorithm for designing cue-based model for dialogue act recognition
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- @Article{Yahya2010190,
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author = "Anwar Ali Yahya and Ramlan Mahmod and
Abd Rahman Ramli",
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title = "Dynamic {Bayesian} networks and variable length
genetic algorithm for designing cue-based model for
dialogue act recognition",
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journal = "Computer Speech \& Language",
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volume = "24",
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number = "2",
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pages = "190--218",
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year = "2010",
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ISSN = "0885-2308",
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DOI = "doi:10.1016/j.csl.2009.04.002",
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URL = "http://www.sciencedirect.com/science/article/B6WCW-4W7B0DH-1/2/29a9b688bd5d374230572940760f5bd2",
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keywords = "genetic algorithms, genetic programming, Dialogue act
recognition, Dynamic Bayesian networks, Variable length
genetic algorithm, Lexical cues selection",
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abstract = "The automatic recognition of dialogue act is a task of
crucial importance for the processing of natural
language dialogue at discourse level. It is also one of
the most challenging problems as most often the
dialogue act is not expressed directly in speaker's
utterance. In this paper, a new cue-based model for
dialogue act recognition is presented. The model is,
essentially, a dynamic Bayesian network induced from
manually annotated dialogue corpus via dynamic Bayesian
machine learning algorithms. Furthermore, the dynamic
Bayesian network's random variables are constituted
from sets of lexical cues selected automatically by
means of a variable length genetic algorithm, developed
specifically for this purpose. To evaluate the proposed
approaches of design, three stages of experiments have
been conducted. In the initial stage, the dynamic
Bayesian network model is constructed using sets of
lexical cues selected manually from the dialogue
corpus. The model is evaluated against two previously
proposed models and the results confirm the
potentiality of dynamic Bayesian networks for dialogue
act recognition. In the second stage, the developed
variable length genetic algorithm is used to select
different sets of lexical cues to constitute the
dynamic Bayesian networks' random variables. The
developed approach is evaluated against some of the
previously used ranking approaches and the results
provide experimental evidences on its ability to avoid
the drawbacks of the ranking approaches. In the third
stage, the dynamic Bayesian networks model is
constructed using random variables constituted from the
sets of lexical cues generated in the second stage and
the results confirm the effectiveness of the proposed
approaches for designing dialogue act recognition
model.",
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notes = "GA applied to variable length 1D lists",
- }
Genetic Programming entries for
Anwar Ali Yahya
Ramlan Mahmod
Abd Rahman Bin Ramli
Citations