MURAL - Maynooth University Research Archive Library



    Semantic Relation Classification: Task Formalisation and Refinement


    Silva, Vivian S., Hürliman, Manuela, Davis, Brian, Handschuh, Siegfried and Freitas, Andre (2016) Semantic Relation Classification: Task Formalisation and Refinement. In: Proceedings of the Workshop on Cognitive Aspects of the Lexicon, 11-17 December 2016, Osaka, Japan.

    [thumbnail of BD_computer science_semantic.pdf]
    Preview
    Text
    BD_computer science_semantic.pdf

    Download (151kB) | Preview

    Abstract

    The identification of semantic relations between terms within texts is a fundamental task in Natural Language Processing which can support applications requiring a lightweight semantic interpretation model. Currently, semantic relation classification concentrates on relations which are evaluated over open-domain data. This work provides a critique on the set of abstract relations used for semantic relation classification with regard to their ability to express relationships between terms which are found in a domain-specific corpora. Based on this analysis, this work proposes an alternative semantic relation model based on reusing and extending the set of abstract relations present in the DOLCE ontology. The resulting set of relations is well grounded, allows to capture a wide range of relations and could thus be used as a foundation for automatic classification of semantic relations.
    Item Type: Conference or Workshop Item (Paper)
    Keywords: Semantic Relation Classification; Task Formalisation; Refinement;
    Academic Unit: Faculty of Science and Engineering > Computer Science
    Item ID: 13396
    Depositing User: Brian Davis
    Date Deposited: 05 Oct 2020 15:47
    Journal or Publication Title: Proceedings of the Workshop on Cognitive Aspects of the Lexicon
    Refereed: Yes
    URI: https://mural.maynoothuniversity.ie/id/eprint/13396
    Use Licence: This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here

    Repository Staff Only (login required)

    Item control page
    Item control page

    Downloads

    Downloads per month over past year

    Origin of downloads