Research Commons
      • Browse 
        • Communities & Collections
        • Titles
        • Authors
        • By Issue Date
        • Subjects
        • Types
        • Series
      • Help 
        • About
        • Collection Policy
        • OA Mandate Guidelines
        • Guidelines FAQ
        • Contact Us
      • My Account 
        • Sign In
        • Register
      View Item 
      •   Research Commons
      • University of Waikato Research
      • Computing and Mathematical Sciences
      • ICADL 2018 Poster Proceedings
      • View Item
      •   Research Commons
      • University of Waikato Research
      • Computing and Mathematical Sciences
      • ICADL 2018 Poster Proceedings
      • View Item
      JavaScript is disabled for your browser. Some features of this site may not work without it.

      Deep feature engineering using full-text publications

      Safder, Iqra; Batool, Hafsa; Hassan, Saeed-Ul
      Thumbnail
      Files
      ICADL2018_paper_63.pdf
      97.14Kb
      DOI
       10.15663/ICADL.2018.63
      Find in your library  
      Citation
      Export citation
      Safder, I., Batool, H., & Hassan, S.-U. (2018). Deep feature engineering using full-text publications. In ICADL Poster Proceedings. Hamilton, New Zealand: The University of Waikato.
      Permanent Research Commons link: https://hdl.handle.net/10289/12178
      Abstract
      We have observed a rapid proliferation in scientific literature and advancements in web technologies has shifted information dissemination to digital libraries [1]. In general, the research conducted by scientific community is articulated through scholarly publications pertaining high quality algorithms along other algorithmic specific metadata such as achieved results, deployed datasets and runtime complexity. According to estimation, approximately 900 algorithms are published in top core conferences during the years 2005-2009 [2]. With this significant increase in algorithms reported in these conferences, more efficient search systems with advance searching capabilities must be designed to search for an algorithm and its supported metadata such as evaluation results like precision, recall etc., particular dataset on which an algorithm executed or the time complexity achieved by that algorithm from full body text of an article. Such advanced search systems could support researchers and software engineers looking for cutting edge algorithmic solutions. Recently, state designed to search for an algorithm from full text articles [3-5].

      In this work, we designed an advanced search engine for full text publications that leverages the deep learning techniques to classify algorithmic specific metadata and further to improve searching capabilities for a search system.
      Date
      2018
      Type
      Conference Contribution
      Publisher
      The University of Waikato
      Rights
      © 2018 copyright with the authors.
      Collections
      • ICADL 2018 Poster Proceedings [8]
      Show full item record  

      Usage

      Downloads, last 12 months
      78
       
       
       

      Usage Statistics

      For this itemFor all of Research Commons

      The University of Waikato - Te Whare Wānanga o WaikatoFeedback and RequestsCopyright and Legal Statement