MURAL - Maynooth University Research Archive Library



    Expert and Corpus-Based Evaluation of a 3-Space Model of Conceptual Blending


    Hurley, Donny and Abgaz, Yalemisew and Hager, Ali and O'Donoghue, Diarmuid (2016) Expert and Corpus-Based Evaluation of a 3-Space Model of Conceptual Blending. In: EGPAI 2016: Evaluating General Purpose AI workshop, 30 August 2016, The Hague, Netherlands.

    [img]
    Preview
    Download (378kB) | Preview


    Share your research

    Twitter Facebook LinkedIn GooglePlus Email more...



    Add this article to your Mendeley library


    Abstract

    This paper presents the 3-space model of conceptual blending that estimates the figurative similarity between Input spaces 1 and 2 using both their analogical similarity and the interconnecting Generic Space. We describe how our Dr Inventor model is being evaluated as a model of lexically based figurative similarity. We describe distinct but related evaluation tasks focused on 1) identifying novel and quality analogies between computer graphics publications 2) evaluation of machine generated translations of text documents 3) evaluation of documents in a plagiarism corpus. Our results show that Dr Inventor is capable of generating novel comparisons between publications but also appears to be a useful tool for evaluating machine translation systems and for detecting and assessing the level of plagiarism between documents. We also outline another more recent evaluation, using a corpus of patent applications.

    Item Type: Conference or Workshop Item (Paper)
    Keywords: Expert and Corpus-Based Evaluation; 3-Space Model; Conceptual Blending; Dr Inventor;
    Academic Unit: Faculty of Science and Engineering > Computer Science
    Item ID: 10345
    Depositing User: Dr. Diarmuid O'Donoghue
    Date Deposited: 20 Dec 2018 17:39
    Refereed: Yes
    Funders: European Union Seventh Framework Programme ([FP7/2007- 2013])
    URI:

      Repository Staff Only(login required)

      View Item Item control page

      Downloads

      Downloads per month over past year