From Argumentation Mining to Stance Classification

Parinaz Sobhani, Diana Inkpen and Stan Matwin

2nd Workshop on Argumentation Mining (ARG-MINING 2015)
Denver, Colorado, USA, June 4, 2015


Argumentation mining and stance classification were recently introduced as interesting tasks in text mining. In this paper, a novel framework for argument tagging based on topic modeling is proposed. Unlike other machine learning approaches for argument tagging which often require large set of labeled data, the proposed model is minimally supervised and merely a one-to-one mapping between the pre-defined argument set and the extracted topics is required. These extracted arguments are subsequently exploited for stance classification. Additionally, a manually-annotated corpus for stance classification and argument tagging of online news comments is introduced and made available. Experiments on our collected corpus demonstrate the benefits of using topic-modeling for argument tagging. We show that using Non-Negative Matrix Factorization instead of Latent Dirichlet Allocation achieves better results for argument classification, close to the results of a supervised classifier. Furthermore, the statistical model that leverages automatically-extracted arguments as features for stance classification shows promising results.

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