Freya Hewett


pdf bib
Automatically evaluating the conceptual complexity of German texts
Freya Hewett | Manfred Stede
Proceedings of the 17th Conference on Natural Language Processing (KONVENS 2021)


pdf bib
The Utility of Discourse Parsing Features for Predicting Argumentation Structure
Freya Hewett | Roshan Prakash Rane | Nina Harlacher | Manfred Stede
Proceedings of the 6th Workshop on Argument Mining

Research on argumentation mining from text has frequently discussed relationships to discourse parsing, but few empirical results are available so far. One corpus that has been annotated in parallel for argumentation structure and for discourse structure (RST, SDRT) are the ‘argumentative microtexts’ (Peldszus and Stede, 2016a). While results on perusing the gold RST annotations for predicting argumentation have been published (Peldszus and Stede, 2016b), the step to automatic discourse parsing has not yet been taken. In this paper, we run various discourse parsers (RST, PDTB) on the corpus, compare their results to the gold annotations (for RST) and then assess the contribution of automatically-derived discourse features for argumentation parsing. After reproducing the state-of-the-art Evidence Graph model from Afantenos et al. (2018) for the microtexts, we find that PDTB features can indeed improve its performance.