Structured prediction models for argumentative claim parsing from text

Abstract

The internet abounds with opinions expressed in text. While a number of natural language processing techniques have been proposed for opinion analysis from text, most offer only a shallow analysis without providing any insights into reasons supporting the opinions. In online discussions, however, opinions are typically expressed as arguments, consisting of a set of claims endowed with internal semantic structure amenable to deeper analysis. In this article, we introduce the task of argumentative claim parsing (ACP), which aims at extracting semantic structures of claims from argumentative text. The task is split into two subtasks: claim segmentation and claim structuring. We present a new dataset on two discussion topics with claims manually annotated for both subtasks. Inspired by structured prediction approaches, we propose a number of supervised machine learning models for the ACP task, including deep learning, chain classifier, and joint learning models. Our experiments reveal that claim segmentation is a relatively feasible task, with the best-performing model achieving up to 0.37 and 0.79 exact and lenient macro-averaged F1-score, respectively. Claim structuring, however, proved to be a more challenging task, with the best-performing models achieving at most 0.08 macro-averaged F1-score.

Keywords

• opinion mining
• argumentation mining
• natural language processing
• machine learning
• deep learning
• structured prediction