KE Group
Topic: Projects
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Publications for topic "Projects" sorted by first author
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| , , , , , , and , ITS+DM Hackathon (ITSC 2017): Lane Departure Prediction with Naturalistic Driving Data (2019), in: IEEE Intelligent Transportation Systems Magazine, 11 |
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| , , and , Auswertung der Ersteinschätzung-Routine-Daten unter Verwendung von statistischen Analyseverfahren zur Früherkennung von epidemischen Gefahrenlagen, in: 15. Jahrestagung Deutsche Gesellschaft Interdisziplinäre Notfall- und Akutmedizin (DGINA), 2020 |
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| , , , and , Evaluation der im Rahmen des ESEG-Projektes implementierten Pflichtelemente für ein EDV-gestütztes Ersteinschätzungssystem in interdisziplinären zentralen Notaufnahmen, in: 15. Jahrestagung Deutsche Gesellschaft Interdisziplinäre Notfall- und Akutmedizin (DGINA), 2020 |
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| , Interaktives Regellernen, TU Darmstadt, Knowledge Engineering Group, 2009 |
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| , and , Extreme Gradient Boosted Multi-label Trees for Dynamic Classifier Chains, Knowledge Engineering Group, Technische Universität Darmstadt, number 2006.08094 [cs.LG], ArXiv e-prints, 2020 |
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| , and , Extreme Gradient Boosted Multi-label Trees for Dynamic Classifier Chains, in: Discovery Science - 23rd International Conference, {DS} 2020, Thessaloniki, Greece, October 19-21, 2020, Proceedings, pages 471--485, Springer International Publishing, 2020 |
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| , and , Graded Multilabel Classification by Pairwise Comparisons, in: 2014 IEEE International Conference on Data Mining (ICDM 2014), pages 731--736, Curran Associates, IEEE, 2014 |
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| , and , Graded Multilabel Classification by Pairwise Comparisons, Knowledge Engineering Group, Technische Universität Darmstadt, Technical Report, 2014 |
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| , Meta-Lernen einer Evaluierungs-Funktion für einen Regel-Lerner, TU Darmstadt, Knowledge Engineering Group, 2006 |
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| , , and , Combining Predictions under Uncertainty: The Case of Random Decision Trees, in: Discovery Science, pages 78--93, Springer, 2021 |
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| , , and , Preference-Based Policy Iteration: Leveraging Preference Learning for Reinforcement Learning, in: Proceedings of the 22nd European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2011, Athens, Greece), Part I, pages 312--327, Springer, 2011 |
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| and , Driver Information Embedding with Siamese LSTM networks, in: 2019 IEEE Intelligent Vehicles Symposium (IV), Paris, France, IEEE, 2019 |
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| and , Using Past Maneuver Executions for Personalization of a Driver Model, in: Proceedings of the 21th IEEE International Conference on Intelligent Transportation Systems (ITSC-18), Maui, Hawaii, pages 742--748, IEEE, 2018 |
| and , Exploiting Maneuver Dependency for Personalization of a Driver Model, in: Proceedings of the Conference ``Lernen, Wissen, Daten, Analysen'' ({LWDA}-18), pages 93--97, CEUR-WS.org, 2018 |
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| , , and , Time-to-Lane-Change Prediction with Deep Learning, in: Proceedings of the 20th IEEE International Conference on Intelligent Transportation Systems (ITSC-17), IEEE, 2017 |
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| , and , Learning Analogy-Preserving Sentence Embeddings for Answer Selection, in: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL), pages 910--919, Association for Computational Linguistics, 2019 |
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| , Deep Multi Instance-Learning zur Syndrom-Erkennung anhand von Daten aus Notaufnahmen, TU Darmstadt, 2021 |
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| , GPU-accelerated Learning of Gradient Boosted Multi-label Classification Rules, TU Darmstadt, 2020 |
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| , Iterative Optimization of Rule Sets, TU Darmstadt, Knowledge Engineering Group, 2010 |
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| , , and , Multi-label LeGo -- Enhancing Multi-label Classifiers with Local Patterns, Knowledge Engineering Group, Technische Universität Darmstadt, number TUD-KE-2012-02, 2012 |
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| , Scalable Histogram-based Induction of Gradient Boosted Multi-label Rules, TU Darmstadt, 2021 |
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| , and , Predicting and Forecasting the Lifetime of Automotive Vehicle Components, in: 29. VDI-Fachtagung Technische Zuverlässigkeit 2019, Düsseldorf, pages 321-336, VDI Wissensforum GmbH, 2019 |
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| , Predicting harmful conditions with Hidden Markov Models, TU Darmstadt, 2017 |
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| , Rule-Based Methods, in: Encyclopedia of Systems Biology, Springer-Verlag, 2013 |
| Proceedings of the ECML/PKDD-13 Workshop on Reinforcement Learning with Generalized Feedback: Beyond Numeric Rewards, 2013 |
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| Proceedings of the ECAI-12 Workshop on Preference Learning: Problems and Applications in AI (PL-12), 2012 |
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| , , and , Preference-based Reinforcement Learning: A Formal Framework and a Policy Iteration Algorithm (2012), in: Machine Learning, 89:1-2(123--156) |
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| , , and , Multilabel Classification via Calibrated Label Ranking (2008), in: Machine Learning, 73:2(133--153) |
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| , , and , Learning Structured Declarative Rule Sets — A Challenge for Deep Discrete Learning, in: 2nd Workshop on Deep Continuous-Discrete Machine Learning (DeCoDeML), 2020 |
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| and , Preference Learning, in: Encyclopedia of the Sciences of Learning, pages 986, Springer-Verlag, 2012 |
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| and , Error-Correcting Output Codes as a Transformation from Multi-Class to Multi-Label Prediction, in: Proceedings of the 15th International Conference on Discovery Science (DS-12), pages 254--267, Springer, 2012 |
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| , Informationsextraktion aus Lebensläufen, TU Darmstadt, Knowledge Engineering Group, 2009 |
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| , Vorhersagen von räumlich korrelierten epidemiologischen Zeitreihen mittels Methoden der Statistik und des maschinellen Lernens, TU Darmstadt, Knowledge Engineering Group, 2019 |
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| , Investigation of the Effect of Meteorological Data on Spatial Surveillance of Disease Outbreaks, TU Darmstadt, Knowledge Engineering Group, 2019 |
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| , Optimizing Rule Selection in Random Forest Simplification, TU Darmstadt, 2020 |
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| , , , , , , and , Risikostratifizierung durch Implementierung und Evaluation eines COVID-19-Scores (2020), in: Medizinische Klinik - Intensivmedizin und Notfallmedizin, 115(132–-138) |
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| and , Editorial: Preference Learning and Ranking (2013), in: Machine Learning, 93:2-3(185--189) |
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| Special Issue on Preference Learning and Ranking (2013), in: Machine Learning, 93:2-3 |
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| and , Learning from Label Preferences, in: Proceedings of the 14th International Conference on Discovery Science (DS-11), pages 2--17, Springer, 2011 |
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| and , Learning from Label Preferences, in: Proceedings of the 22nd International Conference on Algorithmic Learning Theory (ALT-11), pages 38, Springer, 2011 |
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| , , , and , Rule-Based Multi-label Classification: Challenges and Opportunities, in: Rules and Reasoning, pages 3--19, Springer International Publishing, 2020 |
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| , and , Conformal Rule-Based Multi-label Classification, in: KI 2020: Advances in Artificial Intelligence, Springer, Cham, 2020 |
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| , , , and , A Flexible Class of Dependence-sensitive Multi-label Loss Functions (2021), in: Machine Learning Journal |
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| , Machine Learning Approaches for Failure Type Detection and Predictive Maintenance, TU Darmstadt, Knowledge Engineering Group, 2015 |
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| , , and , Towards Rule Learning Approaches to Instance-based Ontology Matching, in: 1st International Workshop on Knowledge Discovery and Data Mining Meets Linked Open Data (Know@LOD), pages 13--18, 2012 |
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| and , On the Quest for Optimal Rule Learning Heuristics (2010), in: Machine Learning, 78:3(343--379) |
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| and , Separate-and-conquer Regression, TU Darmstadt, Knowledge Engineering Group, number TUD-KE-2010-01, 2010 |
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| and , The SeCo-framework for rule learning, TU Darmstadt, Knowledge Engineering Group, number TUD-KE-2010-02, 2010 |
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| and , An Empirical Quest for Optimal Rule Learning Heuristics, TU Darmstadt, Knowledge Engineering Group, number TUD-KE-2008-01, 2008 |
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| and , A Re-Evaluation of the Over-Searching Phenomenon in Inductive Rule Learning, TU Darmstadt, Knowledge Engineering Group, number TUD-KE-2008-02, 2008 |
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| and , Meta-Learning Rule Learning Heuristics, TU Darmstadt, Knowledge Engineering Group, number TUD-KE-2007-02, 2007 |
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| and , Meta-Learning Rule Learning Heuristics, in: Proceedings of the German Workshop on Lernen, Wissen, Adaptivität - LWA2007, pages 167--174, 2007 |
| and , On Trading Off Consistency and Coverage in Inductive Rule Learning, in: Proceedings of the German Workshop on Lernen, Wissen, Adaptivität - LWA2006, pages 306--313, Gesellschaft für Informatik e. V. (GI), 2006 |
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| and , An Empirical Investigation of the Trade-Off between Consistency and Coverage in Rule Learning Heuristics, in: Proceedings of the 11th International Conference on Discovery Science (DS-08), pages 40--51, Springer-Verlag, 2008 |
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| and , Meta-Learning Rule Learning Heuristics, in: Proceedings of ECML-PKDD-07 Workshop on Planning to Learn (PlanLearn-07), pages 9-21, 2007 |
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| and , A Re-evaluation of the Over-Searching Phenomenon in Inductive Rule Learning, in: Proceedings of the SIAM International Conference on Data Mining (SDM-09), pages 329--340, 2009 |
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| and , Heuristic Rule-Based Regression via Dynamic Reduction to Classification, in: Proceedings of the 22nd International Joint Conference on Artificial Intelligence (IJCAI-11), Barcelona, Spain, pages 1330--1335, 2011 |
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| , , , and , Informed Hybrid Game Tree Search (2018), in: IEEE Transactions on Games, 10:1(78--90) |
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| , and , Preference-Based Monte Carlo Tree Search, in: Proceedings of the 41st German Conference on Artficial Intelligence (KI-18), pages 327--340, Springer, 2018 |
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K
| , Boosted Rule Learning for Multi-Label Classification using Stochastic Gradient Descent, TU Darmstadt, 2021 |
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| , Bidirectional Rule Learning, Knowledge Engineering Group, TU Darmstadt, 2010 |
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| , Improving Cargo Train Availability with Predictive Maintenance: An Overview and Prototype Implementation, in: AET Papers Repository, Association for European Transport, 2016 |
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| , Nutzung bahnbezogener Sensordaten zur Vorhersage von Wartungszyklen, TU Darmstadt, Knowledge Engineering Group, 2014 |
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| and , Batchwise Patching of Classifiers, in: Proceedings of the 32nd AAAI Conference on Artificial Intelligence (AAAI-18), pages 3374--3381, 2018 |
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| , and , Predicting Cargo Train Failures: A Machine Learning Approach for a Lightweight Prototype, in: Proceedings of the 19th International Conference on Discovery Science (DS-16), Bari, Italy, pages 151--166, Springer-Verlag, 2016 |
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| , and , Advances in Predictive Maintenance for a Railway Scenario - Project Techlok, Knowledge Engineering Group, 2015 |
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| , and , On the Challenges of Real World Data in Predictive Maintenance Scenarios: A Railway Application, in: Proceedings of the LWA 2015 Workshops: KDML, FGWM, IR, and FGDB, Trier, Germany, October 7-9, 2015., pages 121-132, CEUR Workshop Proceedings, 2015 |
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| , , and , Learning to Predict Component Failures in Trains, in: Proceedings of the 16th LWA Workshops: KDML, IR and FGWM, pages 71--82, CEUR Workshop Proceedings, 2014 |
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| , and , Efficient Discovery of Expressive Multi-label Rules using Relaxed Pruning, in: Discovery Science, pages 367--382, Springer International Publishing, 2019 |
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| , , and , From Local Patterns to Global Models: The LeGo Approach to Data Mining, in: From Local Patterns to Global Models: Proceedings of the ECML/PKDD-08 Workshop (LeGo-08), pages 1--16, 2008 |
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| and , Dynamic Classifier Chain with Random Decision Trees, in: Proceedings of the 21st International Conference of Discovery Science (DS-18), Limassol, Cyprus, pages 33--50, Springer-Verlag, 2018 |
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| , and , Revisiting Non-Specific Syndromic Surveillance (2021), in: CoRR, abs/2101.12246 |
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| , and , Improving the Fusion of Outbreak Detection Methods with Supervised Learning, in: Computational Intelligence Methods for Bioinformatics and Biostatistics - 16th International Meeting, {CIBB} 2019, Bergamo, Italy, September 4-6, 2019, Revised Selected Papers, Bergamo, Italy, pages 55--66, Springer, 2020 |
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| , and , Improving Outbreak Detection with Stacking of Statistical Surveillance Methods, in: Workshop Proceedings of epiDAMIK: Epidemiology meets Data Mining and Knowledge discovery (held in conjunction with ACM SIGKDD 2019), Anchorage, USA, 2019 |
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| , and , Revisiting Non-Specific Syndromic Surveillance, in: Advances in Intelligent Data Analysis {XIX} - 19th International Symposium on Intelligent Data Analysis, {IDA} 2021, Porto, Portugal, April 26-28, 2021, Proceedings, pages 128-140, Springer International Publishing, 2021 |
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| , and , A Unifying Framework and Comparative Evaluation of Statistical and Machine Learning Approaches to Non-Specific Syndromic Surveillance (2021), in: Computers, 10:3 |
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| , , and , Sum-Product Networks for Early Outbreak Detection of Emerging Diseases, in: Artificial Intelligence in Medicine, pages 61--71, Springer International Publishing, 2021 |
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| , Transductive Pairwise Classification, TU Darmstadt, Knowledge Engineeering Group, 2013 |
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| , Segmentation of legal documents, in: Proceedings of the 12th International Conference on Artificial Intelligence and Law, Barcelona, Spain, pages 88--97, ACM, 2009 |
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| , An Evaluation of Multilabel Classification for the Automatic Annotation of Texts, in: Proceedings of the LWA 2010: Lernen, Wissen, Adaptivität, Workshop on Knowledge Discovery, Data Mining and Machine Learning (KDML 2010), Kassel, pages 121-123, 2010 |
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| and , Pairwise Learning of Multilabel Classifications with Perceptrons, in: Proceedings of the 2008 IEEE International Joint Conference on Neural Networks (IJCNN-08), IEEE, pages 2900--2907, 2008 |
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| and , Pairwise Learning of Multilabel Classifications with Perceptrons, TU Darmstadt, Knowledge Engineering Group, number TUD-KE-2007-05, 2007 |
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| and , Efficient Pairwise Multilabel Classification for Large-Scale Problems in the Legal Domain, in: Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Disocvery in Databases (ECML-PKDD-2008), Part II, pages 50--65, Springer, 2008 |
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| and , Efficient Multilabel Classification Algorithms for Large-Scale Problems in the Legal Domain, in: Semantic Processing of Legal Texts -- Where the Language of Law Meets the Law of Language, pages 192-215, Springer-Verlag, 2010 |
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| and , An Evaluation of Efficient Multilabel Classification Algorithms for Large-Scale Problems in the Legal Domain, in: Proceedings of the LWA 2007: Lernen - Wissen - Adaption, pages 126--132, 2007 |
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| , , and , Learning Interpretable Rules for Multi-label Classification, in: Explainable and Interpretable Models in Computer Vision and Machine Learning, pages 81--113, Springer-Verlag, 2018 |
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| and , An Evaluation of Efficient Multilabel Classification Algorithms for Large-Scale Problems in the Legal Domain, in: Proceedings of the LREC 2008 Workshop on Semantic Processing of Legal Texts, pages 23-32, 2008 |
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| and , Learning rules for multi-label classification: a stacking and a separate-and-conquer approach (2016), in: Machine Learning, 105:1(77--126) |
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| , , and , Tree-Based Dynamic Classifier Chains (2021), in: Machine Learning Journal |
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| , and , Efficient Voting Prediction for Pairwise Multilabel Classification (2010), in: Neurocomputing, 73:7-9(1164 - 1176) |
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| , and , Efficient Voting Prediction for Pairwise Multilabel Classification, in: Proceedings of the 17th European Symposium on Artificial Neural Networks (ESANN 2009, Bruges, Belgium), pages 117--122, d-side publications, 2009 |
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| , and , Advances in Efficient Pairwise Multilabel Classification, TU Darmstadt, Knowledge Engineering Group, number TUD-KE-2008-06, 2008 |
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| , and , Efficient Voting Prediction for Pairwise Multilabel Classification, in: Proceedings of the LWA 2009: Lernen - Wissen - Adaption, Workshop Knowledge Discovery, Data Mining and Machine Learning (KDML-09), Darmstadt, Germany, pages 72--75, 2009 |
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| , and , A Data Set for the Analysis of Text Quality Dimensions in Summarization Evaluation, in: Proceedings of the Twelfth International Conference on Language Resources and Evaluation (LREC 2020), pages 6690–-6699, European Language Resources Association, 2020 |
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| , Predictive Maintenance in a Railway Scenario using One-Class Support Vector Machines, TU Darmstadt, 2016 |
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| , , , , and , Learning Context-dependent Label Permutations for Multi-label Classification, in: Proceedings of the 36th International Conference on Machine Learning (ICML-19), pages 4733--4742, {PMLR}, 2019 |
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| , Wissensgewinn aus Spieldatenbanken, Knowledge Engineering Group, TU Darmstadt, 2013 |
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| and , Reliable Multilabel Classification: Prediction with Partial Abstention (2020), in: Proceedings of the AAAI Conference on Artificial Intelligence, 34:04(5264-5271) |
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| , , , and , On Aggregation in Ensembles of Multilabel Classifiers, in: Discovery Science, pages 533--547, Springer International Publishing, 2020 |
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| and , Efficient Implementation of Class-Based Decomposition Schemes for Naive Bayes (2014), in: Machine Learning, 96:3(295--309) |
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| and , Unsupervised Generation of Data Mining Features from Linked Open Data, in: International Conference on Web Intelligence and Semantics (WIMS'12), 2012 |
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| , , and , Correlation-based Discovery of Disease Patterns for Syndromic Surveillance (2021), in: CoRR, abs/2110.09208 |
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| , , and , Correlation-based Discovery of Disease Patterns for Syndromic Surveillance (2021), in: Frontiers in Big Data |
| , and , Simplifying Random Forests: On the Trade-off between Interpretability and Accuracy, Knowledge Engineering Group, Technische Universität Darmstadt, number 1911.04393, ArXiv e-prints, 2019 |
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| , , and , Gradient-Based Label Binning in Multi-Label Classification, in: Machine Learning and Knowledge Discovery in Databases (ECML-PKDD), Springer, 2021 |
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| , and , On the Trade-off Between Consistency and Coverage in Multi-label Rule Learning Heuristics, in: Discovery Science, pages 96--111, Springer International Publishing, 2019 |
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| , , , and , Learning Gradient Boosted Multi-label Classification Rules, in: Machine Learning and Knowledge Discovery in Databases (ECML-PKDD), pages 124--140, Springer, 2020 |
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| , and , Exploiting Anti-monotonicity of Multi-label Evaluation Measures for Inducing Multi-label Rules, in: PAKDD 2018: Advances in Knowledge Discovery and Data Mining, pages 29--42, Springer International Publishing, 2018 |
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| , and , A Hybrid Multi-Strategy Recommender System Using Linked Open Data, in: Semantic Web Evaluation Challenge, Proceedings (ESWC 2014), pages 150-156, Springer, 2014 |
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| , Vergleich von AQ, CN2 und CN2 mit Weighted Covering, TU Darmstadt, Knowledge Engineering Group, 2006 |
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| and , Evaluating and mitigating a Collusive version of the Interest Flooding Attack in NDN, in: Proceedings of the {IEEE} Symposium on Computers and Communication, {ISCC-16}, pages 938--945, {IEEE} Computer Society, 2016 |
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| and , CoMon: An architecture for coordinated caching and cache-aware routing in CCN, in: Proceedings of the 12th Annual {IEEE} Consumer Communications and Networking Conference, (CCNC-15), pages 663--670, {IEEE}, 2015 |
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| , Linking of emergency room and infectious disease data using machine learning approaches, TU Darmstadt, Knowledge Engineering Group, 2020 |
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| , Design und Implementierung einer Entwicklungsumgebung für Regel-Lerner, TU Darmstadt, Knowledge Engineering Group, 2007 |
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| , Paarweise Hierarchische Klassifikation, TU Darmstadt, Knowledge Engineering Group, 2008 |
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| and , Probability Estimation and Aggregation for Rule Learning, TU Darmstadt, Knowledge Engineering Group, number TUD-KE-2010-03, 2010 |
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| and , Rule Stacking: An approach for compressing an ensemble of rule sets into a single classifier, TU Darmstadt, Knowledge Engineering Group, number TUD-KE-2010-05, 2010 |
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| and , An Empirical Comparison of Techniques for Selecting and Combining Local Patterns into a Global Model, TU Darmstadt, Knowledge Engineering Group, number TUD-KE-2008-03, 2008 |
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| and , A Comparison of Techniques for Selecting and Combining Class Association Rules, in: Proceedings of the LWA 2008: Lernen -- Wissen -- Adaption, pages "", 2008 |
| and , Rule Stacking: An Approach for Compressing an Ensemble of Rule Sets into a Single Classifier, in: Proceedings of the 14th International Conference on Discovery Science (DS-11), pages 323--334, Springer, 2011 |
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| and , An Empirical Comparison of Probability Estimation Techniques for Probabilistic Rules, in: Proceedings of the 12th International Conference on Discovery Science (DS-09), Porto, Portugal, pages 317--331, Springer-Verlag, 2009 |
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| and , A Comparison of Techniques for Selecting and Combining Class Association Rules, in: From Local Patterns to Global Models: Proceedings of the ECML/PKDD-08 Workshop (LeGo-08), pages 154--168, 2008 |
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| and , A Study of Probability Estimation Techniques for Rule Learning, in: From Local Patterns to Global Models: Proceedings of the ECML/PKDD-09 Workshop (LeGo-09), pages 123--138, 2009 |
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| , and , Evaluation of Different Heuristics for Accommodating Asymmetric Loss Functions in Regression, in: Proceedings of the 20th International Conference on Discovery Science (DS-17), Kyoto, Japan, Springer-Verlag, 2017 |
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| , and , Multi-target prediction: a unifying view on problems and methods (2019), in: Data Mining and Knowledge Discovery, 33:2(293--324) |
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| , Supervised Local Pattern Discovery, TU Darmstadt, Knowledge Engineering Group, 2008 |
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| , Vergleich von Pruning-Algorithmen für Regel-Lerner, TU Darmstadt, Knowledge Engineering Group, 2008 |
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| , , and , A Survey of Preference-Based Reinforcement Learning Methods (2017), in: Journal of Machine Learning Research, 18:136(1--46) |
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| and , On Learning from Game Annotations (2015), in: IEEE Transactions on Computational Intelligence and AI in Games, 7:3(304-316) |
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| and , Learning from Trajectory-Based Action Preferences, in: Proceedings of the ICRA 2013 Workshop on Autonomous Learning, Karslruhe, 2013 |
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| and , Preference-Based Reinforcement Learning: A Preliminary Survey, in: Proceedings of the ECML/PKDD-13 Workshop on Reinforcement Learning from Generalized Feedback: Beyond Numeric Rewards, 2013 |
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| and , EPMC: Every Visit Preference Monte Carlo for Reinforcement Learning, in: Proceedings of the 5th Asian Conference on Machine Learning, (ACML-13), pages 483--497, JMLR.org, 2013 |
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| and , First Steps Towards Learning from Game Annotations, in: Proceedings of the {ECAI} Workshop on Preference Learning: Problems and Applications in AI, Montpellier, pages 53-58, 2012 |
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| , and , Model-Free Preference-based Reinforcement Learning, in: Proceedings of the 30th {AAAI} Conference on Artificial Intelligence (AAAI-16), Phoenix, Arizona, pages 2222--2228, 2016 |
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| and , Preference Learning from Annotated Game Databases, in: Proceedings of the 16th {LWA} Workshops: KDML, {IR} and FGWM, pages 57--68, CEUR-WS.org, 2014 |
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| and , A Policy Iteration Algorithm for Learning from Preference-based Feedback, in: Advances in Intelligent Data Analysis XII: 12th International Symposium (IDA-13), pages 427--437, Springer-Verlag, 2013 |
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| , Count Modeling with Sum-Product Networks for Syndromic Surveillance, TU Darmstadt, 2021 |
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| and , A Review and Comparison of Strategies for Handling Missing Values in Separate-and-Conquer Rule Learning (2011), in: Journal of Intelligent Information Systems, 36:1(73--98) |
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| and , A Comparison of Strategies for Handling Missing Values in Rule Learning, TU Darmstadt, Knowledge Engineering Group, number TUD-KE-2009-03, 2009 |
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| , Analysis and Comparison of Existent Information Extraction Methods, TU Darmstadt, Knowledge Engineering Group, 2006 |
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| , Estimating Summary Quality with Pairwise Preferences, in: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT 2018), New Orleans, USA, pages 1687-1696, Association for Computational Linguistics, 2018 |
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| , auto-hMDS: Automatic Construction of a Large Heterogeneous Multilingual Multi-Document Summarization Corpus, in: Proceedings of the 11th Edition of the Language Resources and Evaluation Conference (LREC 2018), Miyazaki, Japan, pages 3228-3233, 2018 |
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| , A Comparison Between the Usage of Flat and Structured Game Trees for Move Evaluation in Hearthstone, TU Darmstadt, Knowledge Engineering Group, 2015 |
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| , SeqCluSum: Combining Sequential Clustering and Contextual Importance Measuring to Summarize Developing Events over Time, in: The Twenty-Fourth Text Retrieval Conference Proceedings, Gaithersburg, Maryland, USA, National Institute of Standards and Technology, 2015 |
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| , , , , , , , , and , What’s important in a text? An extensive evaluation of linguistic annotations for summarization, in: Proceedings of the 5th International Conference on Social Networks Analysis, Management and Security (SNAMS-18), Valencia, Spain, pages 272--277, 2018 |
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| , and , Beyond Centrality and Structural Features: Learning Information Importance for Text Summarization, in: Proceedings of the 20th SIGNLL Conference on Computational Natural Language Learning, Berlin, Germany, pages 84-94, Association for Computational Linguistics, 2016 |
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| , and , Sequential Clustering and Contextual Importance Measures for Incremental Update Summarization, in: Proceedings of the 26th International Conference on Computational Linguistics, Osaka, Japan, pages 1071-1082, The COLING 2016 Organizing Committee, 2016 |
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| , and , Which Scores to Predict in Sentence Regression for Text Summarization?, in: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT 2018), pages 1782--1791, 2018 |
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| , and , The Next Step for Multi-Document Summarization: A Heterogeneous Multi-Genre Corpus Built with a Novel Construction Approach, in: Proceedings of the 26th International Conference on Computational Linguistics, Osaka, Japan, pages 1535-1545, The COLING 2016 Organizing Committee, 2016 |
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