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Automatic speaker identification in novels

  • Author / Creator
    He, Hua
  • Speaker identification is the task of attributing utterances to characters in literary narratives. Although only some of the utterances are explicitly attributed in novels, humans readers are able to determine the speakers of the remaining utterances because of their understanding of the plot. This dissertation proposes a method to automatically identify the speakers using supervised machine learning methods that utilize various text clues and a speaker alternation pattern. In addition, the method incorporates an unsupervised actor-topic model that aims to distinguish speakers depending on the content of their statements. The experimental results show that the method substantially outperforms a baseline method, and is competitive and more general when compared to previous approaches to the problem.

  • Subjects / Keywords
  • Graduation date
    2011-11
  • Type of Item
    Thesis
  • Degree
    Master of Science
  • DOI
    https://doi.org/10.7939/R38M5F
  • License
    This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for non-commercial purposes. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.
  • Language
    English
  • Institution
    University of Alberta
  • Degree level
    Master's
  • Department
    • Department of Computing Science
  • Supervisor / co-supervisor and their department(s)
    • Grzegorz Kondrak (Computing Science)
    • Denilson Barbosa (Computing Science)
  • Examining committee members and their departments
    • Denilson Barbosa (Computing Science)
    • Grzegorz Kondrak (Computing Science)
    • Stan Ruecker (English and Film Studies)
    • Davood Rafiei (Computing Science)
    • Mario A. Nascimento (Computing Science)