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Argumentation mining

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Abstract

Argumentation mining aims to automatically detect, classify and structure argumentation in text. Therefore, argumentation mining is an important part of a complete argumentation analyisis, i.e. understanding the content of serial arguments, their linguistic structure, the relationship between the preceding and following arguments, recognizing the underlying conceptual beliefs, and understanding within the comprehensive coherence of the specific topic. We present different methods to aid argumentation mining, starting with plain argumentation detection and moving forward to a more structural analysis of the detected argumentation. Different state-of-the-art techniques on machine learning and context free grammars are applied to solve the challenges of argumentation mining. We also highlight fundamental questions found during our research and analyse different issues for future research on argumentation mining.

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Notes

  1. In a binary classification, accuracy is the proportion of true results (both true positives and true negatives) in the population.

  2. The results presented in Mochales and Moens (2007) were 90%, but the evaluation was done on a previous version of the ECHR corpus. The new version uses the same texts but with an improved human annotation, where a higher agreement between annotators is achieved.

  3. The F 1 (or F-measure) is a measure of a test’s accuracy. It considers both the precision p and the recall r of the test to compute the score: p is the number of correct results divided by the number of all returned results and r is the number of correct results divided by the number of results that should have been returned. The F 1 can be interpreted as a weighted average of the precision and recall, where an F 1 score reaches its best value at 1 and worst score at 0.

  4. http://jscc.jmksf.com/

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Authors and Affiliations

  1. Katholieke Universiteit Leuven, Leuven, Belgium

    Raquel Mochales & Marie-Francine Moens

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  1. Raquel Mochales
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  2. Marie-Francine Moens
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Correspondence to Raquel Mochales.

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Mochales, R., Moens, MF. Argumentation mining. Artif Intell Law 19, 1–22 (2011). https://doi.org/10.1007/s10506-010-9104-x

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