Detecting "Slippery Slope" and Other Argumentative Stances of Opposition Using Tree Kernels in Monologic Discourse

The aim of this study is to propose an innovative methodology to classify argumentative stances in a monologic argumentative context. Particularly, the proposed approach shows that Tree Kernels can be used in combination with traditional textual vectorization to discriminate between different stances of opposition without the need of extracting highly engineered features. This can be useful in many Argument Mining sub-tasks. In particular, this work explores the possibility of classifying opposition stances by training multiple classifiers to reach different degrees of granularity. Noticeably, discriminating support and opposition stances can be particularly useful when trying to detect Argument Schemes, one of the most challenging sub-task in the Argument Mining pipeline. In this sense, the approach can be also considered as an attempt to classify stances of opposition that are related to specific Argument Schemes.