Patterns of Argumentation Strategies across Topics
K. Al Khatib, H. Wachsmuth, M. Hagen, B. Stein, in: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, 2017, pp. 1362–1368.

WAT-SL: A Customizable Web Annotation Tool for Segment Labeling
J. Kiesel, H. Wachsmuth, K. Al Khatib, B. Stein, in: Proceedings of the Software Demonstrations of the 15th Conference of the European Chapter of the Association for Computational Linguistics, 2017, pp. 13–16.

"Page Rank'' for Argument Relevance
H. Wachsmuth, B. Stein, Y. Ajjour, in: Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers, 2017, pp. 1117–1127.

Building an Argument Search Engine for the Web
H. Wachsmuth, M. Potthast, K. Al-Khatib, Y. Ajjour, J. Puschmann, J. Qu, J. Dorsch, V. Morari, J. Bevendorff, B. Stein, in: Proceedings of the 4th Workshop on Argument Mining, 2017, pp. 49–59.

The Impact of Modeling Overall Argumentation with Tree Kernels
H. Wachsmuth, G. Da San Martino, D. Kiesel, B. Stein, in: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, 2017, pp. 2369–2379.

Computational Argumentation Quality Assessment in Natural Language
H. Wachsmuth, N. Naderi, Y. Hou, Y. Bilu, V. Prabhakaran, T.A. Thijm, G. Hirst, B. Stein, in: Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers, 2017, pp. 176–187.

A Universal Model for Discourse-Level Argumentation Analysis
H. Wachsmuth, B. Stein, Special Section of the ACM Transactions on Internet Technology: Argumentation in Social Media (2017) 1–24.

Argumentation Quality Assessment: Theory vs. Practice
H. Wachsmuth, N. Naderi, I. Habernal, Y. Hou, G. Hirst, I. Gurevych, B. Stein, in: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), 2017, pp. 250–255.

Webis at the CLEF 2017 Dynamic Search Lab
M. Hagen, J. Kiesel, M. Alshomary, B. Stein, in: Working Notes of CLEF 2017 - Conference and Labs of the Evaluation Forum, 2017.

How to Get Endorsements? Predicting Facebook Likes Using Post Content and User Engagement
W.-F. Chen, Y.-P. Chen, L.-W. Ku, in: International Conference on HCI in Business, Government, and Organizations, 2017, pp. 190–202.

A News Editorial Corpus for Mining Argumentation Strategies
K. Al Khatib, H. Wachsmuth, J. Kiesel, M. Hagen, B. Stein, in: Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, 2016, pp. 3433–3443.

Cross-Domain Mining of Argumentative Text through Distant Supervision
K. Al-Khatib, H. Wachsmuth, M. Hagen, J. Köhler, B. Stein, in: Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2016, pp. 1395–1404.

Using Argument Mining to Assess the Argumentation Quality of Essays
H. Wachsmuth, K. Al Khatib, B. Stein, in: Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, 2016, pp. 1680–1691.

Pipelines Für Effiziente und Robuste Ad-hoc Textanalyse
H. Wachsmuth, in: Ausgezeichnete Informatikdissertationen 2015, 2016, pp. 329–338.

UTCNN: a Deep Learning Model of Stance Classification on Social Media Text
W.-F. Chen, L.-W. Ku, in: Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics, 2016, pp. 1635–1645.

WordForce: Visualizing Controversial Words in Debates
W.-F. Chen, F.-Y. Lin, L.-W. Ku, in: Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: System Demonstrations, 2016, pp. 273–277.

Chinese Textual Sentiment Analysis: Datasets, Resources and Tools
L.-W. Ku, W.-F. Chen, in: Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Tutorial Abstracts, 2016, pp. 5–8.

Sentiment Flow - A General Model of Web Review Argumentation
H. Wachsmuth, J. Kiesel, B. Stein, in: J. Tsujii, J. Hajic (Eds.), Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, 2015, pp. 601–611.

Text Analysis Pipelines - Towards Ad-hoc Large-scale Text Mining
H. Wachsmuth, Text Analysis Pipelines - Towards Ad-Hoc Large-Scale Text Mining, 2015.

Pipelines for Ad-hoc Large-scale Text Mining
H. Wachsmuth, Pipelines for Ad-Hoc Large-Scale Text Mining, 2015.