Johannes Daxenberger

We investigate neural techniques for end-to-end computational argumentation mining (AM). Expand

We investigate the temporal dynamics underlying emergent roles on individual and organizational levels. Expand

We perform a qualitative analysis across six different datasets and show that these appear to conceptualize claims quite differently. Expand

We present the results from an automatic classification experiment, based on an annotated corpus of edits in the English Wikipedia. Expand

We show how to leverage the power of contextualized word embeddings to classify and cluster topic-dependent arguments, achieving impressive results on both tasks and across multiple datasets. Expand

We propose a 21-category classification scheme for edits based on Faigley and Witte’s (1981) model. Expand

We identify four categories of temporal role-taking behaviors, that differ in their mobility between emergent roles and across Wikipedia articles. Expand

We present ArgumenText, which we believe is the first system for topic-relevant argument search in heterogeneous texts. Expand

This paper describes our submissions to the GermEval 2017 Shared Task, which focused on the analysis of customer feedback about the Deutsche Bahn AG. Expand

We present DKPro TC, a framework for supervised learning experiments on textual data. Expand