Publikationen des Institutes

Zeige Ergebnisse 169 - 210 von 248

2019


Potthast, M., Gienapp, L., Euchner, F., Heilenkötter, N., Weidmann, N., Wachsmuth, H., Stein, B., & Hagen, M. (2019). Argument Search: Assessing Argument Relevance. In SIGIR 2019: Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval (S. 1117-1120). Association for Computing Machinery, Inc. https://doi.org/10.1145/3331184.3331327
Skitalinskaya, G., Klaff, J., & Spliethöver, M. (2019). CLEF ProtestNews Lab 2019: Contextualized word embeddings for event sentence detection and event extraction. CEUR Workshop Proceedings, 2380. https://ceur-ws.org/Vol-2380/paper_118.pdf
Spliethöver, M., Klaff, J., & Heuer, H. (2019). Is It Worth the Attention? A Comparative Evaluation of Attention Layers for Argument Unit Segmentation. In B. Stein, & H. Wachsmuth (Hrsg.), Proceedings of the 6th Workshop on Argument Mining (S. 74-82). Association for Computational Linguistics. https://doi.org/10.18653/v1/W19-4509
Stein, B., & Wachsmuth, H. (2019). Introduction. In B. Stein, & H. Wachsmuth (Hrsg.), Proceedings of the 6th Workshop on Argument Mining Association for Computational Linguistics (ACL).
Stein, B., & Wachsmuth, H. (2019). Introduction. In B. Stein, & H. Wachsmuth (Hrsg.), Proceedings of the 6th Workshop on Argument Mining (S. III-III). Association for Computational Linguistics (ACL). https://aclanthology.org/W19-4500
Stein, B., & Wachsmuth, H. (Hrsg.) (2019). Proceedings of the 6th Workshop on Argument Mining. Association for Computational Linguistics (ACL). https://aclanthology.org/W19-45
Tornede, A., Wever, M., & Hüllermeier, E. (2019). Algorithm Selection as Recommendation: From Collaborative Filtering to Dyad Ranking. In 29th Workshop Computational Intelligence https://ris.uni-paderborn.de/download/15011/17060/ci_workshop_tornede.pdf
Wachsmuth, H. (2019). Argumentation Mining. By Manfred Stede and Jodi Schneider (University of Potsdam, University of Illinois at Urbana-Champaign). Morgan & Claypool (Synthesis Lectures on Human Language Technologies, edited by Graeme Hirst, volume 40), 2018, xvi+175 pp; paperback, ISBN 978-1-68173-459-0; ebook, ISBN 978-1-68173-460-6; doi:10.2200/S00883ED1V01Y201811HLT040: Argumentation Mining. Computational Linguistics, 45(3). https://doi.org/10.1162/coli_r_00358
Wever, M., Mohr, F., Tornede, A., & Hüllermeier, E. (2019). Automating Multi-Label Classification Extending ML-Plan. In ICML 2019 Workshop AutoML https://ris.uni-paderborn.de/download/10232/13177/Automating_MultiLabel_Classification_Extending_ML-Plan.pdf

2018


Ajjour, Y., Wachsmuth, H., Kiesel, D., Riehmann, P., Fan, F., Castiglia, G., Adejoh, R., Fröhlich, B., & Stein, B. (2018). Visualization of the Topic Space of Argument Search Results in args.me. In E. Blanco, & W. Lu (Hrsg.), Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (System Demonstrations) (S. 60-65). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/d18-2011
Al-Khatib, K., Wachsmuth, H., Lang, K., Herpel, J., Hagen, M., & Stein, B. (2018). Modeling Deliberative Argumentation Strategies on Wikipedia. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Long Papers) (S. 2545-2555). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/p18-1237
Biedenkapp, A., Marben, J., Lindauer, M., & Hutter, F. (2018). CAVE: Configuration Assessment, Visualization and Evaluation. In P. M. Pardalos, R. Battiti, M. Brunato, & I. Kotsireas (Hrsg.), Learning and Intelligent Optimization (S. 115-130). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Band 11353 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-030-05348-2_10
Bonfert, M., Spliethöver, M., Arzaroli, R., Lange, M., Hanci, M., & Porzel, R. (2018). If You Ask Nicely: A Digital Assistant Rebuking Impolite Voice Commands. In Proceedings of the 20th ACM International Conference on Multimodal Interaction (S. 95-102). Association for Computing Machinery (ACM). https://doi.org/10.1145/3242969.3242995
Chen, W. F., Wachsmuth, H., Al-Khatib, K., & Stein, B. (2018). Learning to Flip the Bias of News Headlines. In Proceedings of the 11th International Conference on Natural Language Generation (S. 79-88). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/W18-6509
Eggensperger, K., Lindauer, M., Hoos, H. H., Hutter, F., & Leyton-Brown, K. (2018). Efficient benchmarking of algorithm configurators via model-based surrogates. Machine learning, 107(1), 15-41. Vorabveröffentlichung online. https://doi.org/10.1007/s10994-017-5683-z
Eggensperger, K., Lindauer, M., & Hutter, F. (2018). Neural Networks for Predicting Algorithm Runtime Distributions. In J. Lang (Hrsg.), Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence (S. 1442-1448). AAAI Press/International Joint Conferences on Artificial Intelligence. https://doi.org/10.24963/ijcai.2018/200
El Baff, R., Wachsmuth, H., Al-Khatib, K., & Stein, B. (2018). Challenge or Empower: Revisiting Argumentation Quality in a News Editorial Corpus. In Proceedings of the 22nd Conference on Computational Natural Language Learning (CoNLL 2018) (S. 454-464). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/k18-1044
Feurer, M., Eggensperger, K., Falkner, S., Lindauer, M. T., & Hutter, F. (2018). Practical Automated Machine Learning for the AutoML Challenge 2018. https://www.tnt.uni-hannover.de/papers/data/1407/18-AUTOML-AutoChallenge.pdf
Habernal, I., Wachsmuth, H., Gurevych, I., & Stein, B. (2018). Before Name-calling: Dynamics and Triggers of Ad Hominem Fallacies in Web Argumentation. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers) (S. 386-396). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/N18-1036
Habernal, I., Wachsmuth, H., Gurevych, I., & Stein, B. (2018). SemEval-2018 Task 12: The Argument Reasoning Comprehension Task. In M. Apidianaki, M. Apidianaki, S. M. Mohammad, J. May, E. Shutova, S. Bethard, & M. Carpuat (Hrsg.), Proceedings of the 12th International Workshop on Semantic Evaluation (S. 763-772). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/S18-1121
Habernal, I., Wachsmuth, H., Gurevych, I., & Stein, B. (2018). The Argument Reasoning Comprehension Task: Identification and Reconstruction of Implicit Warrants. In M. Walker, H. Ji, & A. Stent (Hrsg.), Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers) (S. 1930-1940). Association for Computational Linguistics (ACL). https://doi.org/10.48550/arXiv.1708.01425, https://doi.org/10.18653/v1/N18-1175
Kiesel, D., Riehmann, P., Fan, F., Ajjour, Y., Wachsmuth, H., Stein, B., & Fröhlich, B. (2018). Improving Barycentric Embeddings of Topics Spaces. In IEEE VIS 2018 IEEE.
Lindauer, M., Hoos, H., Hutter, F., & Leyton-Brown, K. (2018). Selection and Configuration of Parallel Portfolios. In Handbook of Parallel Constraint Reasoning (S. 583-615). Springer International Publishing AG. https://doi.org/10.1007/978-3-319-63516-3_15
Lindauer, M. T., van Rijn, J. N., & Kotthoff, L. (2018). The Algorithm Selection Competition Series 2015-17. Vorabveröffentlichung online. https://arxiv.org/abs/1805.01214v1
Lindauer, M., & Hutter, F. (2018). Warmstarting of Model-Based Algorithm Configuration. In 32nd AAAI Conference on Artificial Intelligence, AAAI 2018 (S. 1355-1362). (Proceedings of the AAAI Conference on Artificial Intelligence). AAAI Press/International Joint Conferences on Artificial Intelligence. https://arxiv.org/abs/1709.04636v3
Wachsmuth, H., Stede, M., El Baff, R., Al-Khatib, K., Skeppstedt, M., & Stein, B. (2018). Argumentation Synthesis following Rhetorical Strategies. In E. M. Bender, L. Derczynski, & P. Isabelle (Hrsg.), Proceedings of the 27th International Conference on Computational Linguistics (S. 3753-3765). Association for Computational Linguistics (ACL). https://aclanthology.org/C18-1318
Wachsmuth, H., Syed, S., & Stein, B. (2018). Retrieval of the Best Counterargument without Prior Topic Knowledge. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (S. 241-251). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/p18-1023
Wagner, M., Lindauer, M., Mısır, M., Nallaperuma, S., & Hutter, F. (2018). A case study of algorithm selection for the traveling thief problem. Journal of heuristics, 24(3), 295-320. Vorabveröffentlichung online. https://doi.org/10.1007/s10732-017-9328-y

2017


Ajjour, Y., Chen, W. F., Kiesel, J., Wachsmuth, H., & Stein, B. (2017). Unit Segmentation of Argumentative Texts. In I. Habernal, I. Gurevych, K. Ashley, C. Cardie, N. Green, D. Litman, G. Petasis, C. Reed, N. Slonim, & V. Walker (Hrsg.), Proceedings of the 4th Workshop on Argument Mining (S. 118-128). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/W17-5115
Al-Khatib, K., Wachsmuth, H., Hagen, M., & Stein, B. (2017). Patterns of Argumentation Strategies across Topics. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing (S. 1351-1357). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/d17-1141
Biedenkapp, A., Lindauer, M., Eggensperger, K., Hutter, F., Fawcett, C., & Hoos, H. H. (2017). Efficient Parameter Importance Analysis via Ablation with Surrogates. In Proceedings of the AAAI Conference on Artificial Intelligence Vorabveröffentlichung online. https://doi.org/10.1609/aaai.v31i1.10657
Hutter, F., Lindauer, M., Balint, A., Bayless, S., Hoos, H., & Leyton-Brown, K. (2017). The Configurable SAT Solver Challenge (CSSC). Artificial intelligence, 243, 1-25. Vorabveröffentlichung online. https://doi.org/10.1016/j.artint.2016.09.006
Kiesel, J., Wachsmuth, H., Al-Khatib, K., & Stein, B. (2017). WAT-SL: A Customizable Web Annotation Tool for Segment Labeling. In A. Penas, & A. Martins (Hrsg.), Proceedings of the EACL 2017 Software Demonstrations (S. 13-16). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/e17-3004
Lindauer, M., Hutter, F., Hoos, H. H., & Schaub, T. (2017). AutoFolio: An Automatically Configured Algorithm Selector (Extended Abstract). In C. Sierra (Hrsg.), International Joint Conference on Artificial Intelligence (IJCAI 2017) (S. 5025-5029). AAAI Press/International Joint Conferences on Artificial Intelligence. https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=&ved=2ahUKEwjo2uHc_87qAhVpzMQBHf2lDTwQFjABegQIAxAB&url=https%3A%2F%2Fwww.ijcai.org%2FProceedings%2F2017%2F0715.pdf&usg=AOvVaw1ART0bWLbCU4uLc4oV19yv
Lindauer, M. T., van Rijn, J. N., & Kotthoff, L. (2017). Open Algorithm Selection Challenge 2017 Setup and Scenarios. http://proceedings.mlr.press/v79/lindauer17a/lindauer17a.pdf
Wachsmuth, H., Naderi, N., Habernal, I., Hou, Y., Hirst, G., Gurevych, I., & Stein, B. (2017). Argumentation Quality Assessment: Theory vs. Practice. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Short Papers) (S. 250-255). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/P17-2039
Wachsmuth, H., & Stein, B. (2017). A Universal Model for Discourse-Level Argumentation Analysis. ACM Transactions on Internet Technology, 17(3), Artikel 28. https://doi.org/10.1145/2957757
Wachsmuth, H., Potthast, M., Al-Khatib, K., Ajjour, Y., Puschmann, J., Qu, J., Dorsch, J., Morari, V., Bevendorff, J., & Stein, B. (2017). Building an Argument Search Engine for the Web. In I. Habernal, I. Gurevych, K. Ashley, C. Cardie, N. Green, D. Litman, G. Petasis, C. Reed, N. Slonim, & V. Walker (Hrsg.), Proceedings of the 4th Workshop on Argument Mining (S. 49-59). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/W17-5106
Wachsmuth, H., Naderi, N., Hou, Y., Bilu, Y., Prabhakaran, V., Thijm, T. A., Hirst, G., & Stein, B. (2017). Computational Argumentation Quality Assessment in Natural Language. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers (S. 176-187). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/e17-1017
Wachsmuth, H., Stein, B., & Ajjour, Y. (2017). “PageRank” for Argument Relevance. In P. Blunsom, A. Koller, & M. Lapata (Hrsg.), Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics, Long Papers (S. 1117-1127). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/e17-1105
Wachsmuth, H., da San Martino, G., Kiesel, D., & Stein, B. (2017). The Impact of Modeling Overall Argumentation with Tree Kernels. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing (S. 2379-2389). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/d17-1253
Wagner, M., Friedrich, T., & Lindauer, M. (2017). Improving local search in a minimum vertex cover solver for classes of networks. In 2017 IEEE Congress on Evolutionary Computation (CEC): Proceedings (S. 1704-1711). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/cec.2017.7969507