Dora Kiesel


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Visualization of the Topic Space of Argument Search Results in
Yamen Ajjour | Henning Wachsmuth | Dora Kiesel | Patrick Riehmann | Fan Fan | Giuliano Castiglia | Rosemary Adejoh | Bernd Fröhlich | Benno Stein
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing: System Demonstrations

In times of fake news and alternative facts, pro and con arguments on controversial topics are of increasing importance. Recently, we presented as the first search engine for arguments on the web. In its initial version, ranked arguments solely by their relevance to a topic queried for, making it hard to learn about the diverse topical aspects covered by the search results. To tackle this shortcoming, we integrated a visualization interface for result exploration in that provides an instant overview of the main aspects in a barycentric coordinate system. This topic space is generated ad-hoc from controversial issues on Wikipedia and argument-specific LDA models. In two case studies, we demonstrate how individual arguments can be found easily through interactions with the visualization, such as highlighting and filtering.


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The Impact of Modeling Overall Argumentation with Tree Kernels
Henning Wachsmuth | Giovanni Da San Martino | Dora Kiesel | Benno Stein
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

Several approaches have been proposed to model either the explicit sequential structure of an argumentative text or its implicit hierarchical structure. So far, the adequacy of these models of overall argumentation remains unclear. This paper asks what type of structure is actually important to tackle downstream tasks in computational argumentation. We analyze patterns in the overall argumentation of texts from three corpora. Then, we adapt the idea of positional tree kernels in order to capture sequential and hierarchical argumentative structure together for the first time. In systematic experiments for three text classification tasks, we find strong evidence for the impact of both types of structure. Our results suggest that either of them is necessary while their combination may be beneficial.