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In recent years, argumentation mining, which automatically extracts the structure of argumentation from unstructured documents such as essays and debates, is gaining attention. For argumentation mining applications, argumentcomponent classification is an important subtask. The existing methods can be classified into supervised methods and unsupervised methods. Many existing supervised methods use a classifier to identify the roles of argument components, such as“ claim”or“ premise”, but many of them use information of a single sentence without relying on the whole document. On the other hand, existing unsupervised document classification has the advantage of being able to use the whole document, but accuracy of these methods is not so high. In this paper, we propose a method for argument-component classification that combines relation identification by neural networks and TextRank to integrate relation informations (i.e. the strength of the relation). This method can use argumentation-specific knowledge by employing supervised learning on a corpus while maintaining the advantage of using the whole document. Experiments on two corpora, one consisting of student essays and the other of Wikipedia articles, show the effectiveness of this method.

Argument Component Classification by Relation Identification by Neural Network and TextRank