An Analysis of Linear Argumentation Structure of Korean Debate Texts Using Sequential Modeling and Linguistic Features

Sangah Lee, Hyopil Shin

http://doi.org/10.5626/JOK.2018.45.12.1292

Current studies on argument mining provide tree-structured argumentation structures based on relational nuclearities and discourse relations between sentences in each document. In this case, inconsistencies between related sentences may occur, constructing a full argumentation structure for a document by the bottom-up method. This paper introduces relations between the topic of texts and sentences to provide a frame of argumentation structure. Automatic analysis of argumentation structure uses contextual information from documents, as argument types defined for each sentence are applied to the sequential model. In this paper, we vectorized sentences using bag-of-words of morphemes, word embedding of morphemes, and some linguistic features extracted from the sentence respectively, and used those vectors as inputs of models to predict argument types in the document. As a result, the combination of linguistic features and the sequential model revealed the best result in the experiment, showing 0.68 as the f1-score.