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

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

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

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

H. Wachsmuth, J. Kiesel, and B. Stein, “Sentiment Flow - A General Model of Web Review Argumentation,” in Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, 2015, pp. 601–611.

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

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

H. Wachsmuth, M. Trenkmann, B. Stein, G. Engels, and T. Palakarska, “A Review Corpus for Argumentation Analysis,” in Proceedings of the 15th International Conference on Intelligent Text Processing and Computational Linguistics, 2014, pp. 115–127.

F. Brüseke, H. Wachsmuth, G. Engels, and S. Becker, “PBlaman: performance blame analysis based on Palladio contracts,” in Proceedings of the 4th International Symposium on Autonomous Minirobots for Research and Edutainment, 2014, no. 12, pp. 1975–2004.

H. Wachsmuth, M. Trenkmann, B. Stein, and G. Engels, “Modeling Review Argumentation for Robust Sentiment Analysis,” in Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers, 2014, pp. 553–564.

H. Wachsmuth, B. Stein, and G. Engels, “Learning Efficient Information Extraction on Heterogeneous Texts,” in Proceedings of the Sixth International Joint Conference on Natural Language Processing, 2013, pp. 534–542.

H. Wachsmuth, B. Stein, and G. Engels, “Information Extraction as a Filtering Task,” in Proceedings of the 22nd ACM International Conference on Conference on Information &; Knowledge Management, 2013, pp. 2049–2058.

H. Wachsmuth, M. Rose, and G. Engels, “Automatic Pipeline Construction for Real-Time Annotation,” in 14th International Conference on Intelligent Text Processing and Computational Linguistics, 2013, pp. 38–49.

H. Wachsmuth and B. Stein, “Optimal Scheduling of Information Extraction Algorithms,” in Proceedings of COLING 2012: Posters, 2012, pp. 1281–1290.

H. Wachsmuth and K. Bujna, “Back to the Roots of Genres: Text Classification by Language Function,” in Proceedings of 5th International Joint Conference on Natural Language Processing, 2011, pp. 632–640.

H. Wachsmuth, B. Stein, and G. Engels, “Constructing Efficient Information Extraction Pipelines,” in 20th ACM International Conference on Information and Knowledge Management, 2011, pp. 2237–2240.

H. Wachsmuth, P. Prettenhofer, and B. Stein, “Efficient Statement Identification for Automatic Market Forecasting,” in Proceedings of the 23rd International Conference on Computational Linguistics (Coling 2010), 2010, pp. 1128–1136.

S. Arens et al., “Smart Teams: Simulating Large Robotic Swarms in Vast Environments,” in Proceedings of the 4th International Symposium on Autonomous Minirobots for Research and Edutainment, 2007, pp. 215–222.