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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argumentationmining.disi.unibo.it
273 Citations
Highly Influential Citations
18
Background Citations
146
Methods Citations
38
Results Citations
4

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273 Citations

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TLDR
This survey article will review the existing systems and applications and will cover the three categories of argumentation models and a comparative table depicting the most frequently applied ML method.
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Mining Bipolar Argumentation Frameworks from Natural Language Text
TLDR
A methodology for mining topic-dependent Bipolar Argumentation Frameworks (BAFs) from natural language text by identifying attack and support argumentative relations between texts about the same topic is described.
Combining argumentation and aspect-based opinion mining: The SMACk system
TLDR
SMACk, the authors' opinion summary system built on top of an argumentation framework with the aim to exchange, communicate and resolve possibly conflicting viewpoints, allows the user to extract debated opinions from a set of documents containing user-generated content from online commercial websites.
Social Media Argumentation Mining: The Quest for Deliberateness in Raucousness
TLDR
The motivation for social media argumentation mining, as well as the tasks and challenges involved, are discussed.
Five Years of Argument Mining: a Data-driven Analysis
TLDR
This paper presents the argument mining tasks, and the obtained results in the area from a data-driven perspective, and highlights the main weaknesses suffered by the existing work in the literature, and proposes open challenges to be faced in the future.
Argumentation Mining in User-Generated Web Discourse
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.
Automatic argumentation mining and the role of stance and sentiment
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.
Cross-Lingual Argumentation Mining
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.
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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
This work presents different methods to aid argumentation mining, starting with plain argumentation detection and moving forward to a more structural analysis of the detected argumentation.
NoDE: A Benchmark of Natural Language Arguments
TLDR
NoDE is presented, a benchmark of natural language arguments composed of three datasets, built from different textual sources and annotated highlighting positive and negative connections between arguments.
Mining Economic Sentiment Using Argumentation Structures
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.
Applying Kernel Methods to Argumentation Mining
TLDR
It is shown that a classification accuracy of 65%, can be attained using Natural Language Processing based kernel approaches, which do not require any heuristic choice of features.
Argumentation Mining on the Web from Information Seeking Perspective
TLDR
It is argued that an annotation scheme for argumentation mining is a function of the task requirements and the corpus properties and it is found that the choice of the argument components to be annotated strongly depends on the register, the length of the document, and inherently on the literary devices and structures used for expressing argumentation.
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TLDR
This paper presents a novel framework which allows to mine opinions from Twitter based on incrementally generated queries, and will be able to obtain an "opinion tree", rooted in the first original query.
Combining Textual Entailment and Argumentation Theory for Supporting Online Debates Interactions
TLDR
This paper combines textual entailment with argumentation theory to automatically extract the arguments from debates and to evaluate their acceptability.
Towards Argument Mining from Dialogue
TLDR
This paper introduces theoretical foundations for dialogical argument mining as well as showing the initial implementation in a software for dialogue processing, and the application in corpus analysis.
Argument Extraction from News
TLDR
Evaluation results are quite promising, suggesting that distributed representations can contributory to the task of argument extraction, and a new manually annotated corpus has been constructed that can beused freely for research purposes.
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