Hierarchical document categorization using associative networks

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    Associative networks are a connectionist language model with the ability to handle dynamic data. We used two associative networks to categorize random sets of related Wikipedia articles with only their raw text as input. We then compared the resulting categorization to a gold standard: the manual categorization by Wikipedia authors and used a neural network as a baseline. We also determined a human rating by having a group of judges rank the four categorization methods by correctness and by usefulness with regards to finding information. Based on these tests, we determined that associative networks produce results that are clearly better than the neural network baseline, coming close to the gold standard in terms of usefulness and correctness. Furthermore, automated testing suggests these results continue to hold for large datasets.
    Original languageEnglish
    Title of host publicationProceedings of the IASTED Multiconferences
    Subtitle of host publicationArtificial Intelligence and Applications (AIA 2013)
    EditorsE.P. Klement, W. Borutzky, T. Fahringer, M.H. Hamza, V. Uskov
    Place of PublicationAnaheim
    PublisherACTA Press
    Number of pages8
    ISBN (Print)978-0-88986-955-4
    Publication statusPublished - Feb 2013
    Event12th IASTED International Conference on Artificial Intelligence and Applications, AIA 2013 - Innsbruck, Austria
    Duration: 11 Feb 201313 Feb 2013
    Conference number: 12

    Publication series

    PublisherACTA Press


    Conference12th IASTED International Conference on Artificial Intelligence and Applications, AIA 2013
    Abbreviated titleAIA


    • EWI-23553
    • Document Clustering
    • Hierarchical Categorization
    • METIS-297757
    • Connectionist Language Model
    • Associative Networks
    • Automatic Categorization
    • IR-87091

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