Zeige Ergebnisse 169 - 210 von 269
2020
Syed, S., Chen, W. F., Hagen, M., Stein, B., Wachsmuth, H., & Potthast, M. (2020). Task Proposal: Abstractive Snippet Generation for Web Pages. In B. Davis, Y. Graham, J. Kelleher, & Y. Sripada (Hrsg.), Proceedings of The 13th International Conference on Natural Language Generation (S. 237-241). Association for Computational Linguistics (ACL). https://aclanthology.org/2020.inlg-1.30
Tornede, A., Wever, M., & Hüllermeier, E. (2020). Extreme Algorithm Selection with Dyadic Feature Representation. In A. Appice, G. Tsoumakas, Y. Manolopoulos, & S. Matwin (Hrsg.), Discovery Science - 23rd International Conference, DS 2020, Proceedings: DS 2020: Discovery Science (Band 12323, S. 309-324). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Band 12323 LNAI). https://doi.org/10.1007/978-3-030-61527-7_21
Tornede, A., Wever, M., Werner, S., Mohr, F., & Hüllermeier, E. (2020). Run2Survive: A Decision-theoretic Approach to Algorithm Selection based on Survival Analysis. In Proceedings of The 12th Asian Conference on Machine Learning https://proceedings.mlr.press/v129/tornede20a.html
Tornede, A., Wever, M., & Hüllermeier, E. (2020). Towards Meta-Algorithm Selection. (4th Workshop on Meta-Learning at NeurIPS 2020). Vorabveröffentlichung online. http://arxiv.org/abs/2011.08784v1
Wachsmuth, H., & Werner, T. (2020). Intrinsic Quality Assessment of Arguments. In D. Scott, N. Bel, & C. Zong (Hrsg.), COLING 2020 - 28th International Conference on Computational Linguistics, Proceedings of the Conference (S. 6739-6745). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/2020.coling-main.592
Wever, M., Tornede, A., Mohr, F., & Hüllermeier, E. (2020). LiBRe: Label-Wise Selection of Base Learners in Binary Relevance for Multi-Label Classification. In Lecture Notes in Computer Science: IDA 2020: Advances in Intelligent Data Analysis XVIII https://link.springer.com/chapter/10.1007/978-3-030-44584-3_44
2019
Ajjour, Y., Wachsmuth, H., Kiesel, J., Potthast, M., Hagen, M., & Stein, B. (2019). Data Acquisition for Argument Search: The args.me Corpus. In C. Benzmüller, & H. Stuckenschmidt (Hrsg.), KI 2019: Advances in Artificial Intelligence: 42nd German Conference on AI, Proceedings (S. 48-59). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Band 11793 LNAI). Springer Verlag. https://doi.org/10.1007/978-3-030-30179-8_4
Ajjour, Y., Alshomary, M., Wachsmuth, H., & Stein, B. (2019). Modeling Frames in Argumentation. In K. Inui, J. Jiang, V. Ng, & X. Wan (Hrsg.), Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) (S. 2922-2932). Association for Computational Linguistics. https://doi.org/10.18653/v1/D19-1290
Alshomary, M., & Wachsmuth, H. (2019). Siamese Neural Network for Same Side Stance Classification. In Same Side Stance Classification Shared Task 2019: Proceedings of the Same Side Stance Classification Shared Task organized as a part of the 6th Workshop on Argument Mining (ArgMining 2019) and co-located with the the 57th Annual Meeting of the Association for Computational Linguistics (ACL19) (S. 12-16). (CEUR Workshop Proceedings; Band 2921). https://ceur-ws.org/Vol-2921/paper1.pdf
Alshomary, M., Völske, M., Licht, T., Wachsmuth, H., Stein, B., Hagen, M., & Potthast, M. (2019). Wikipedia Text Reuse: Within and Without. In B. Stein, N. Fuhr, L. Azzopardi, P. Mayr, D. Hiemstra, & C. Hauff (Hrsg.), Advances in Information Retrieval: 41st European Conference on IR Research, ECIR 2019, Proceedings (S. 747-754). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Band 11437 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-030-15712-8_49
Biedenkapp, A., Bozkurt, H. F., Hutter, F., & Lindauer, M. (2019). Towards White-box Benchmarks for Algorithm Control. Vorabveröffentlichung online. https://arxiv.org/abs/1906.07644
Eggensperger, K., Lindauer, M., & Hutter, F. (2019). Pitfalls and Best Practices in Algorithm Configuration. Journal of Artificial Intelligence Research, 64, 861-893. https://doi.org/10.1613/jair.1.11420
El Baff, R., Wachsmuth, H., Al-Khatib, K., Stede, M., & Stein, B. (2019). Computational Argumentation Synthesis as a Language Modeling Task. In Proceedings of The 12th International Conference on Natural Language Generation (S. 54-64). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/W19-8607
Fuks, L., Awad, N., Hutter, F., & Lindauer, M. (2019). An evolution strategy with progressive episode lengths for playing games. In S. Kraus (Hrsg.), Proceedings of the 28th International Joint Conference on Artificial Intelligence, IJCAI 2019 (S. 1234-1240). (IJCAI International Joint Conference on Artificial Intelligence). AAAI Press/International Joint Conferences on Artificial Intelligence. https://doi.org/10.24963/ijcai.2019/172
Lindauer, M. T. (2019). Automated Algorithm Selection –Predict which algorithm to use!. https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=&ved=2ahUKEwjd94W5k9TqAhWM_KQKHVbABogQFjAAegQIARAB&url=http%3A%2F%2Fceur-ws.org%2FVol-2360%2Fpaper2Keynote.pdf&usg=AOvVaw0h4cvTGwQg-XD97fhGaytv
Lindauer, M., Eggensperger, K., Feurer, M., Biedenkapp, A., Marben, J., Müller, P., & Hutter, F. (2019). BOAH: A Tool Suite for Multi-Fidelity Bayesian Optimization & Analysis of Hyperparameters. Vorabveröffentlichung online. https://arxiv.org/pdf/1908.06756
Lindauer, M. T. (2019). Hands-On Automated Machine Learning Tools: Auto-Sklearn and Auto-PyTorch. https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=&cad=rja&uact=8&ved=2ahUKEwibqKyPlNTqAhUwMewKHR7PC0oQFjAAegQIARAB&url=https%3A%2F%2Fwww.automl.org%2Fevents%2Famir19-key-note-and-automl-hands-on%2F&usg=AOvVaw1ggAEDpu7zdnlhnRtLdGQD
Lindauer, M., van Rijn, J. N., & Kotthoff, L. (2019). The algorithm selection competitions 2015 and 2017. Artificial intelligence, 272, 86-100. https://doi.org/10.1016/j.artint.2018.10.004
Lindauer, M., Feurer, M., Eggensperger, K., Biedenkapp, A., & Hutter, F. (2019). Towards Assessing the Impact of Bayesian Optimization’s Own Hyperparameters. In DSO Workshop at IJCAI Vorabveröffentlichung online. https://arxiv.org/abs/1908.06674
Mendoza, H., Klein, A., Feurer, M., Springenberg, J. T., Urban, M., Burkart, M., Dippel, M., Lindauer, M. T., & Hutter, F. (2019). Towards Automatically-Tuned Deep Neural Networks. In Automated Machine Learning https://doi.org/10.1007/978-3-030-05318-5_7
Mohr, F., Wever, M., Tornede, A., & Hüllermeier, E. (2019). From Automated to On-The-Fly Machine Learning. In INFORMATIK 2019: 50 Jahre Gesellschaft für Informatik–Informatik für Gesellschaft https://dl.gi.de/handle/20.500.12116/24989
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. In CLEF 2019 Working Notes: Working Notes of CLEF 2019 - Conference and Labs of the Evaluation Forum (CEUR Workshop Proceedings; Band 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. 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
