Dynamic Bayesian networks and variable length genetic algorithm for designing cue-based model for dialogue act recognition

Created by W.Langdon from gp-bibliography.bib Revision:1.4420

  author =       "Anwar Ali Yahya and Ramlan Mahmod and 
                 Abd Rahman Ramli",
  title =        "Dynamic {Bayesian} networks and variable length
                 genetic algorithm for designing cue-based model for
                 dialogue act recognition",
  journal =      "Computer Speech \& Language",
  volume =       "24",
  number =       "2",
  pages =        "190--218",
  year =         "2010",
  ISSN =         "0885-2308",
  DOI =          "doi:10.1016/j.csl.2009.04.002",
  URL =          "http://www.sciencedirect.com/science/article/B6WCW-4W7B0DH-1/2/29a9b688bd5d374230572940760f5bd2",
  keywords =     "genetic algorithms, genetic programming, Dialogue act
                 recognition, Dynamic Bayesian networks, Variable length
                 genetic algorithm, Lexical cues selection",
  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
  notes =        "GA applied to variable length 1D lists",

Genetic Programming entries for Anwar Ali Yahya Ramlan Mahmod Abd Rahman Bin Ramli