Argumentation mining in user-generated web discourse
Ivan Habernal,Iryna Gurevych +1 more
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The findings show that argumentation mining in user-generated Web discourse is a feasible but challenging task and offers the data, source codes, and annotation guidelines to the community under free licenses.Abstract:
The goal of argumentation mining, an evolving research field in computational linguistics, is to design methods capable of analyzing people's argumentation. In this article, we go beyond the state of the art in several ways. i We deal with actual Web data and take up the challenges given by the variety of registers, multiple domains, and unrestricted noisy user-generated Web discourse. ii We bridge the gap between normative argumentation theories and argumentation phenomena encountered in actual data by adapting an argumentation model tested in an extensive annotation study. iii We create a new gold standard corpus 90k tokens in 340 documents and experiment with several machine learning methods to identify argument components. We offer the data, source codes, and annotation guidelines to the community under free licenses. Our findings show that argumentation mining in user-generated Web discourse is a feasible but challenging task.read more
Citations
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The Uses Of Argument
TL;DR: The the uses of argument is universally compatible with any devices to read, and is available in the digital library an online access to it is set as public so you can download it instantly.
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Parsing Argumentation Structures in Persuasive Essays
Christian Stab,Iryna Gurevych +1 more
TL;DR: The authors identify argument components using sequence labeling at the token level and apply a new joint model for detecting argumentation structures, which is a novel approach for parsing argumentation structure, and apply it to the problem of argumentation parsing.
Journal ArticleDOI
Argument Mining: A Survey
John Lawrence,Chris Reed +1 more
TL;DR: The techniques that establish the foundations for argument mining are explored, a review of recent advances in argument mining techniques are provided, and the challenges faced in automatically extracting a deeper understanding of reasoning expressed in language in general are discussed.
Proceedings ArticleDOI
Which argument is more convincing? Analyzing and predicting convincingness of Web arguments using bidirectional LSTM
Ivan Habernal,Iryna Gurevych +1 more
TL;DR: This work annotates a large datasets of 16k pairs of arguments over 32 topics and investigates whether the relation “A is more convincing than B” exhibits properties of total ordering; these findings are used as global constraints for cleaning the crowdsourced data.
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Transforming the communication between citizens and government through AI-guided chatbots
TL;DR: This paper presents a novel approach, as well as the architecture of an ICT platform supporting it, for the advanced exploitation of a specific AI technology, namely chatbots, in the public sector in order to address a crucial issue: the improvement of communication between government and citizens.
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