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Text Document Topical Recursive Clustering and Automatic Labeling of a Hierarchy of Document Clusters

  • Author / Creator
    Li, Xiaoxiao
  • The overwhelming amount of textual documents currently available highlights the need for information organization and discovery. Effectively organizing documents into a hierarchy of topics and subtopics makes it easier for users to browse the documents. This thesis borrows community mining techniques from social network analysis to generate a hierarchy of topically coherent document clusters. It focuses on giving the document clusters descriptive labels. We propose to use different centrality measures in networks of co-occurring terms to label the document clusters. We also incorporate keyphrase extraction and automatic titling in cluster labeling. The results show that the cluster labeling method utilizing KEA to extract keyphrases from the documents generates the best labels overall comparing to other methods and baselines. We also built an interactive browsing web interface for users to examine the taxonomies.

  • Subjects / Keywords
  • Graduation date
    2012-09
  • Type of Item
    Thesis
  • Degree
    Master of Science
  • DOI
    https://doi.org/10.7939/R3HQ14
  • 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)
    • Zaiane, Osmar (Computing Science)
  • Examining committee members and their departments
    • Zaiane, Osmar (Computing Science)
    • Rathi, Dinesh (Library and Information Studies)
    • Lin, Guohui (Computing Science)