Authors:
Tobias Eljasik-Swoboda
1
;
Felix Engel
2
and
Matthias Hemmje
2
Affiliations:
1
Faculty of Mathematics and Computer Science, University of Hagen, Hagen and Germany
;
2
FTK e.v. Forschungsinstitut für Telekommunikation und Kooperation, Dortmund and Germany
Keyword(s):
Argument Stance Detection, Explainability, Machine Learning, Trainer-athlete Pattern, Ontology Creation, Support Vector Machines, Text Analytics, Architectural Concepts.
Related
Ontology
Subjects/Areas/Topics:
Architectural Concepts
;
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Business Analytics
;
Computational Intelligence
;
Data Engineering
;
Data Management and Quality
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Methodologies and Methods
;
Neural Networks
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Signal Processing
;
Soft Computing
;
Support Vector Machines and Applications
;
Text Analytics
;
Theory and Methods
Abstract:
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 reco
gnition: General features and topic specific features.
(More)