Using Topic Specific Features for Argument Stance Recognition
Tobias Eljasik-Swoboda, Felix Engel, Matthias Hemmje · 2019
Argument detection and its representation through ontologies are important parts of today’s attempt in automated recognition and processing of useful information in the vast amount of constantly produced data. However, due to the highly complex nature of an argument and its characteristics, its automated recognition is hard to implement. Given this overall challenge, as part of the objectives of the RecomRatio project, we are interested in the traceable, automated stance detection of arguments, to enable the construction of explainable pro/con argument ontologies. In our research, we design and evaluate an explainable machine learning based classifier, trained on two publicly available data sets. The evaluation results proved that explainable argument stance recognition is possible with up to .96 F1 when working within the same set of topics and .6 F1 when working with entirely different topics. This informed our hypothesis, that there are two sets of features in argument stance recognition: General features and topic specific features.