Mining Argumentative Structure from Natural Language text using Automatically Generated Premise-Conclusion Topic Models

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This paper presents a method of extracting argumentative structure from natural language text. The approach presented is based on the way in which we understand an argument being made, not just from the words said, but from existing contextual knowledge and understanding of the broader issues. We leverage highprecision, low-recall techniques in order to automatically build a large corpus of inferential statements related to the text’s topic. These statements are then used to produce a matrix representing the inferential relationship between different aspects of the topic. From this matrix, we are able to determine connectedness and directionality of inference between statements in the original text. By following this approach, we obtain results that compare favourably to those of other similar techniques to classify premise-conclusion pairs (with results 22 points above baseline), but without the requirement of large volumes of annotated, domain specific data.
Original languageEnglish
Title of host publicationProceedings of the 4th Workshop on Argument Mining
Place of PublicationPennslyvania
PublisherAssociation for Computational Linguistics
Number of pages10
ISBN (Electronic)9781945626845
Publication statusPublished - 2017
EventEMNLP 2017: Conference on Empirical Methods in Natural Language Processing - Copenhagen, Denmark
Duration: 7 Sep 201711 Sep 2017


ConferenceEMNLP 2017

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