TARGER: Neural argument mining at your fingertips

Artem Chernodub, Oleksiy Oliynyk, Philipp Heidenreich, Alexander Bondarenko, Matthias Hagen, Chris Biemann, Alexander Panchenko

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    39 Citations (Scopus)

    Abstract

    We present TARGER, an open source neural argument mining framework for tagging arguments in free input texts and for keyword-based retrieval of arguments from an argument-tagged web-scale corpus. The currently available models are pre-trained on three recent argument mining datasets and enable the use of neural argument mining without any reproducibility effort on the user's side. The open source code ensures portability to other domains and use cases, such as an application to search engine ranking that we also describe shortly.

    Original languageEnglish
    Title of host publicationACL 2019 - 57th Annual Meeting of the Association for Computational Linguistics, Proceedings of System Demonstrations
    PublisherAssociation for Computational Linguistics (ACL)
    ISBN (Electronic)9781950737499
    Publication statusPublished - 2019
    Event57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, ACL 2019 - Florence, Italy
    Duration: 28 Jul 20192 Aug 2019

    Publication series

    NameACL 2019 - 57th Annual Meeting of the Association for Computational Linguistics, Proceedings of System Demonstrations

    Conference

    Conference57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, ACL 2019
    Country/TerritoryItaly
    CityFlorence
    Period28/07/192/08/19

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