Using Argumentation to Improve Classification in Natural Language Problems

Abstract

Argumentation has proven successful in a number of domains, including Multi-Agent Systems and decision support in medicine and engineering. We propose its application to a domain yet largely unexplored by argumentation research: computational linguistics. We have developed a novel classification methodology that incorporates reasoning through argumentation with supervised learning. We train classifiers and then argue about the validity of their output. To do so, we identify arguments that formalise prototypical knowledge of a problem and use them to correct misclassifications. We illustrate our methodology on two tasks. On the one hand, we address cross-domain sentiment polarity classification , where we train classifiers on one corpus, for example, Tweets, to identify positive/negative polarity and classify instances from another corpus, for example, sentences from movie reviews. On the other hand, we address a form of argumentation mining that we call Relation-based Argumentation Mining , where we classify pairs of sentences based on whether the first sentence attacks or supports the second or whether it does neither. Whenever we find that one sentence attacks/supports the other, we consider both to be argumentative, irrespective of their stand-alone argumentativeness. For both tasks, we improve classification performance when using our methodology, compared to using standard classifiers only.