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… 
View on ACM
argumentationmining.disi.unibo.it
257 Citations
Highly Influential Citations
17
Background Citations
140
Methods Citations
36
Results Citations
4

Figures and Tables from this paper

257 Citations

MARGOT: A web server for argumentation mining

The evolution of argumentation mining: From models to social media and emerging tools

Argumentation Mining: Techniques and Emerging Trends

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.

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.

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.

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.

Crowdsourcing argumentation structures in Chinese hotel reviews

TLDR
As the first Chinese argumentation dataset, this corpus includes 4814 argument component annotations and 411 argument relation annotations, and its annotations qualities are comparable to some widely used argumentation corpora in other languages.
...

References

SHOWING 1-10 OF 111 REFERENCES

Context-Independent Claim Detection for Argument Mining

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.

Integrating argumentation and sentiment analysis for mining opinions from Twitter

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.

Argumentation mining: the detection, classification and structure of arguments in text

TLDR
This paper analyzes the main research questions when dealing with argumentation mining and the different methods studied and developed in order to successfully confront the challenges of argumentationmining in legal texts.

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.
...