Identification of Argumentative Relations in Popular Science Texts

Abstract

The presented work describes the analysis of argumentative statements included into the same text topic fragment as a recognition feature in terms of its efficiency. This study is performed with the purpose of using this feature in automatic recognition of argumentative structures presented in the popular science texts written in Russian. The topic model of a text is constructed based on superphrasal units (text fragments united by one topic) that are identified by detecting clusters of words and word-combinations with the use of scan statistics. Potential relations, extracted from topic models, are verified through the use of texts with manually annotated argumentation structures. The comparison between potential (based on topic models) and manually constructed relations is performed automatically. Macro-average scores of precision and recall are equal to 48.6% and 76.2% correspondingly.

Comparative Analysis of Reasoning in Russian Classic Poetry

“…Nowadays, there is a relatively new computational approach to argument analysis [6], which has been actively evolving since 2014; it deals with systematizing and classifying argumentation schemas used in everyday or professional prosaic communication. The corpuses of argumentation mining are most frequently based on English texts, although several works present the strategies and results of creating corpuses for Russian texts: [8][9][10]. Computational approaches to argument analysis differ in typologies of arguments relevant to natural language texts [11,12].…”