Structured prediction models for argumentative claim parsing from text
Structured prediction models for argumentative claim parsing from text
Blog Article
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 3 Piece Outdoor Sectional 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 Shaver 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.