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

  • Speaker alternation pattern
  • Speaker identification

• Graduation date

  Fall 2011

• 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.