Argumentation Mining

@article{Lippi2016ArgumentationM,
  title={Argumentation Mining},
  author={Marco Lippi and Paolo Torroni},
  journal={ACM Transactions on Internet Technology (TOIT)},
  year={2016},
  volume={16},
  pages={1 - 25}
}
Argumentation mining aims at automatically extracting structured arguments from unstructured textual documents. It has recently become a hot topic also due to its potential in processing information originating from the Web, and in particular from social media, in innovative ways. Recent advances in machine learning methods promise to enable breakthrough applications to social and economic sciences, policy making, and information technology: something that only a few years ago was unthinkable… 
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270 Citations
Highly Influential Citations
18
Background Citations
146
Methods Citations
38
Results Citations
4

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270 Citations

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The motivation for social media argumentation mining, as well as the tasks and challenges involved, are discussed.
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TLDR
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.
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TLDR
Argumentation mining is a subfield of Computational Linguistics that aims at automatically finding arguments and their structural components in natural language text and two other applications that are popular in computational linguistics: sentiment analysis and stance detection are described.
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TLDR
This document presents an argumentation mining system to extract arguments from news articles or essays, in Portuguese and English, using a cross-language machine learning approach based on deep neural networks.
Argument Mining: A Survey
TLDR
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.
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References

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TLDR
This work proposes a method that exploits structured parsing information to detect claims without resorting to contextual information, and yet achieve a performance comparable to that of state-of-the-art methods that heavily rely on the context.
Argumentation mining
TLDR
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TLDR
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TLDR
A framework is proposed that allows the incorporation of information on argumentation structure in the models for economic sentiment discovery in text based on their statistical properties and possibly combined with numeric data.
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TLDR
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TLDR
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TLDR
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TLDR
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TLDR
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