Corpus ID: 212657561

A Dataset Independent Set of Baselines for Relation Prediction in Argument Mining

@article{Cocarascu2020ADI,
  title={A Dataset Independent Set of Baselines for Relation Prediction in Argument Mining},
  author={O. Cocarascu and Elena Cabrio and S. Villata and F. Toni},
  journal={ArXiv},
  year={2020},
  volume={abs/2003.04970}
}
Argument Mining is the research area which aims at extracting argument components and predicting argumentative relations (i.e.,support and attack) from text. In particular, numerous approaches have been proposed in the literature to predict the relations holding between the arguments, and application-specific annotated resources were built for this purpose. Despite the fact that these resources have been created to experiment on the same task, the definition of a single relation prediction… Expand

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References

SHOWING 1-10 OF 43 REFERENCES
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. Expand
Argument Mining with Structured SVMs and RNNs
TLDR
A novel factor graph model for argument mining, designed for settings in which the argumentative relations in a document do not necessarily form a tree structure, which outperform unstructured baselines in both web comments and argumentative essay datasets. Expand
Never Retreat, Never Retract: Argumentation Analysis for Political Speeches
TLDR
This work applies argumentation mining techniques, in particular relation prediction, to study political speeches in monological form, where there is no direct interaction between opponents, to support researchers in history, social and political sciences which must deal with an increasing amount of data in digital form. Expand
Using Argumentation to Improve Classification in Natural Language Problems
TLDR
A novel classification methodology that incorporates reasoning through argumentation with supervised learning is developed, which improves classification performance when using this methodology, compared to using standard classifiers only. Expand
Cross-topic Argument Mining from Heterogeneous Sources
TLDR
This paper proposes a new sentential annotation scheme that is reliably applicable by crowd workers to arbitrary Web texts and shows that integrating topic information into bidirectional long short-term memory networks outperforms vanilla BiLSTMs in F1 in two- and three-label cross-topic settings. Expand
Joint prediction in MST-style discourse parsing for argumentation mining
TLDR
This model not only outperforms two reasonable baselines and two datadriven models of global argument structure for the difficult subtask of relation identification, but also improves the results for central claim identification and function classification and it compares favorably to a complex mstparser pipeline. Expand
From Argument Diagrams to Argumentation Mining in Texts: A Survey
TLDR
The authors provide a critical survey of the literature on both the resulting representations i.e., argument diagramming techniques and on the various aspects of the automatic analysis process. Expand
SENTIWORDNET: A Publicly Available Lexical Resource for Opinion Mining
TLDR
SENTIWORDNET is a lexical resource in which each WORDNET synset is associated to three numerical scores Obj, Pos and Neg, describing how objective, positive, and negative the terms contained in the synset are. Expand
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. Expand
A Corpus of eRulemaking User Comments for Measuring Evaluability of Arguments
TLDR
An argument mining corpus annotated with argumentative structure information capturing the evaluability of arguments is presented, which is a resource for building argument mining systems that can not only extract arguments from unstructured text, but also identify what additional information is necessary for readers to understand and evaluate a given argument. Expand
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