• myGriffith
    • Staff portal
    • Contact Us⌄
      • Future student enquiries 1800 677 728
      • Current student enquiries 1800 154 055
      • International enquiries +61 7 3735 6425
      • General enquiries 07 3735 7111
      • Online enquiries
      • Staff phonebook
    View Item 
    •   Home
    • Griffith Research Online
    • Conference outputs
    • View Item
    • Home
    • Griffith Research Online
    • Conference outputs
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Browse

  • All of Griffith Research Online
    • Communities & Collections
    • Authors
    • By Issue Date
    • Titles
  • This Collection
    • Authors
    • By Issue Date
    • Titles
  • Statistics

  • Most Popular Items
  • Statistics by Country
  • Most Popular Authors
  • Support

  • Contact us
  • FAQs
  • Admin login

  • Login
  • Word Mover’s Distance for Agglomerative Short Text Clustering

    Author(s)
    Franciscus, N
    Ren, X
    Wang, J
    Stantic, B
    Griffith University Author(s)
    Wang, John
    Stantic, Bela
    Year published
    2019
    Metadata
    Show full item record
    Abstract
    In the era of information overload, text clustering plays an important part in the analysis processing pipeline. Partitioning high-quality texts into unseen categories tremendously helps applications in information retrieval, databases, and business intelligence domains. Short texts from social media environment such as tweets, however, remain difficult to interpret due to the broad aspects of contexts. Traditional text similarity approaches only rely on the lexical matching while ignoring the semantic meaning of words. Recent advances in distributional semantic space have opened an alternative approach in utilizing high-quality ...
    View more >
    In the era of information overload, text clustering plays an important part in the analysis processing pipeline. Partitioning high-quality texts into unseen categories tremendously helps applications in information retrieval, databases, and business intelligence domains. Short texts from social media environment such as tweets, however, remain difficult to interpret due to the broad aspects of contexts. Traditional text similarity approaches only rely on the lexical matching while ignoring the semantic meaning of words. Recent advances in distributional semantic space have opened an alternative approach in utilizing high-quality word embeddings to aid the interpretation of text semantics. In this paper, we investigate the word mover’s distance metrics to automatically cluster short text using the word semantic information. We utilize the agglomerative strategy as the clustering method to efficiently group texts based on their similarity. The experiment indicates the word mover’s distance outperformed other standard metrics in the short text clustering task.
    View less >
    Conference Title
    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Volume
    11431
    DOI
    https://doi.org/10.1007/978-3-030-14799-0_11
    Subject
    Artificial intelligence
    Science & Technology
    Computer Science, Artificial Intelligence
    Computer Science, Information Systems
    Computer Science, Theory & Methods
    Publication URI
    http://hdl.handle.net/10072/392638
    Collection
    • Conference outputs

    Footer

    Disclaimer

    • Privacy policy
    • Copyright matters
    • CRICOS Provider - 00233E
    • TEQSA: PRV12076

    Tagline

    • Gold Coast
    • Logan
    • Brisbane - Queensland, Australia
    First Peoples of Australia
    • Aboriginal
    • Torres Strait Islander