Martin Riedl

• Martin Riedl and Chris Biemann (2018): Using Semantics for Granularities of Tokenization, Computational Linguistics, vol. 44, no. 3, p.483-524
• Flavio Massimiliano Cecchini, Martin Riedl, Elisabetta Fersini, Chris Biemann (2018): A comparison of graph-based word sense induction clustering algorithms in a pseudoword evaluation framework, Language Resources and Evaluation (LRE), p. 1-38
• Martin Riedl (2016): Unsupervised Methods for Learning and Using Semantics of Natural Language, Dissertation, TU Darmstadt
• 2015 Sunny Mitra, Ritwik Mitra, Suman Kalyan Maity, Martin Riedl, Chris Biemann, Pawan Goyal and Animesh Mukherjee (2015): An automatic approach to identify word sense changes in text media across timescale, Natural Language Engineering (NLE), Special issue on Graph Methods for NLP, Vol. 21, p. 773-798
• Chris Biemann, Martin Riedl (2013): Text: Now in 2D! A Framework for Lexical Expansion with Contextual Similarity. Journal of Language Modelling (JLM) 1(1), p.55-95
• Martin Riedl and Chris Biemann (2012): Text Segmentation with Topic Models. Journal for Language Technology and Computational Linguistics (JLCL), vol. 27, no. 1, p. 47-70
• Martin Riedl (2010): Using protein identification data to improve mass spec- trometry feature extraction, Master Thesis, HS Mannheim
• Martin Riedl (2009): Usage of data mining to learn activity recognitions rules (in the field of ambient assisted living), Diploma Thesis, HS Mannheim
• Peter Findeisen, Diamandula Sismanidis, Martin Riedl, Victor Costina and Michael Neumaier (2005): Preanalytical impact of sample handling on pro- teome profiling experiments with matrix-assisted laser desorption/ionization time-of-flight mass spectrometry. Clinical chemistry, 51(12):2409-11

Konferenzen

• Martin Riedl, Daniela Betz, Sebastian Padó (2019): Clustering-Based Article Identification in Historical Newspapers, In: Proceedings of the 3rd Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature at NAACL 2019, Minneapolis, USA
• Martin Riedl, Sebastian Padó (2018): A Named Entity Recognition Shootout for German, In Proceedings of the Annual Meeting of the Association for Computational Linguistics (ACL), Melbourne, Australia
• Ahmed Elsafty, Martin Riedl, Chris Biemann (2018): Document-based Recommender System for Job Postings using Dense Representations, In Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies in the Industry Track (NAACL-HLT 2018). New Orleans, LO, USA
• Seid Muhie Yimam, Sanja Štajner, Martin Riedl, Chris Biemann (2017): Complex Word Identification Task across Three Text Genres and Two User Groups. In Proceedings of The 8th International Joint Conference on Natural Language Processing (IJCNLP). Taipei, Taiwan
• Martin Riedl, Chris Biemann (2017): There’s no ‘Count or Predict’ but task-based selection for distributional models. In Proceedings of the 12th International Conference on Computational Semantics (IWCS). Montpellier, France
• Seid Muhie Yimam, Sanja Štajner, Martin Riedl, Chris Biemann (2017): Multilingual and Cross-Lingual Complex Word Identification. In Proceedings of The 2017 International Conference on Recent Advances in Natural Language Processing (RANLP). Varna, Bulgaria
• Prasanth Kolachina, Martin Riedl, Chris Biemann (2017): Replacing OOV Words For Dependency Parsing With Distributional Semantics. Proceedings of the Nordic Conference on Computational Linguistics (NoDaLiDa 2017), Gothenburg, Sweden
• Flavio M. Cecchini, Martin Riedl, Chris Biemann (2017): Using Pseudowords for Algorithm Comparison: An Evaluation Framework for Graph-based Word Sense Induction. Proceedings of the Nordic Conference on Computational Linguistics (NoDaLiDa 2017), Gothenburg, Sweden
• 2016 Martin Riedl, Tim Feuerbach and Chris Biemann (2016): Running into Brick Walls Attempting to Improve a Simple Unsupervised Parser, In: Proceedings of the Conference on Natural Language Processing (KONVENS 2016), p. 215-220, Bochum, Germany
• Alexander Panchenko, Johannes Simon, Martin Riedl and Chris Biemann (2016): Noun Sense Induction and Disambiguation using Graph-Based Distri- butional Semantics, In: Proceedings of the Conference on Natural Language Processing (KONVENS 2016), p. 192-202, Bochum, Germany
• Martin Riedl and Chris Biemann (2016): Unsupervised Compound Splitting With Distributional Semantics Rivals Supervised Methods. In: Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT 2016), p. 617-622, San Diego, CA, USA
• Martin Riedl and Chris Biemann (2015): A Single Word is not Enough: Ranking Multiword Expressions Using Distributional Semantics. In: Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing (EMNLP 2015), p. 2430-2440, Lisboa, Portugal
• Tim Feuerbach, Martin Riedl and Chris Biemann (2015): Distributional Semantics for Resolving Bridging Mentions. In: Proceedings of the Conference on Recent Advances in Natural Language Processing (RANLP ’15), p. 192-199, Hissar, Bulgaria
• Eugen Ruppert, Jonas Klesy, Martin Riedl, Chris Biemann (2015): Rule- based Dependency Parse Collapsing and Propagation for German and English. In: Proceedings of the International Confernce of the German Society for Computational Linguistics and Language Technology (GSCL 2015), p. 58-66, Duisburg, Germany
• Eugen Ruppert, Manuel Kaufmann, Martin Riedl and Chris Biemann (2015): JOBIMVIZ: A Web-based Visualization for Graph-based Distributional Seman- tic Models. In: Proceedings of the Annual Meeting of the Association for Computational Linguistics (ACL) System Demonstrations, p. 103-108, Beijing, China
• Martin Riedl, Michael Glass and Alfio Gliozzo (2014): Lexical Substitution for the Medical Domain. In: Proceedings of Conference on Empirical Methods in Natural Language Processing (EMNLP 2014), p. 610-614, Doha, Qatar
• Martin Riedl, Irina Alles, Chris Biemann (2014), Combining Supervised and Unsupervised Parsing for Distributional Similarity. In: Proceedings of the 25th International Conference on Computational Linguistics (COLING 2014), p. 1435-1446, Dublin, Ireland
• Sunny Mitra, Ritwik Mitra,Martin Riedl, Chris Biemann, Animesh Mukherjee and Pawan Goyal (2014): That’s sick dude!: Automatic identification of word sense change across different timescales. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (ACL 2014), p. 1020-1029, Baltimore, MD, USA
• Martin Riedl, Richard Steuer and Chris Biemann (2014): Distributed Dis- tributional Similarities of Google Books over Centuries. In: Proceedings of the Fourth International Conference on Language Resources and Evaluation (LREC 2014), p. 1401-1405, Reykjavik, Iceland
• Martin Riedl and Chris Biemann (2013): Scaling to Large Data: An efficient and effective method to compute Distributional Thesauri. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP 2013), p. 884-890, Seattle, WA, USA
• Martin Riedl and Chris Biemann (2012): How Text Segmentation Algorithms Gain from Topic Models. In: Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT 2012), p. 553-557, Montreal, Canada
• Martin Riedl and Chris Biemann (2012): TopicTiling: A Text Segmentation Algorithm based on LDA. In: Proceddings of the Student Research Workshop of the 50th Meeting of the Association for Computational Linguistics, p. 37-42, Jeju, South Korea

Workshops

• Martin Riedl and Chris Biemann (2016): Impact of MWE Resources on Multiword Recognition. In: Proceedings of the 12th Workshop on Multiword Expressions in conjunction with ACL 2016, p. 107-111, Berlin, Germany
• Seid Muhie Yimam and Héctor Martínez Alonso and Martin Riedl and Chris Biemann (2016): Learning Paraphrasing for Multiword Expressions. In: Pro- ceedings of the 12th Workshop on Multiword Expressions in conjunction with ACL 2016, p. 1-10, Berlin, Germany
• Alfio Gliozzo, Chris Biemann Chris, Martin Riedl, Bonaventura Coppola, Michael R. Glass and Matthew Hatem (2013): JoBimText Visualizer: A Graph- based Approach to Contextualizing Distributional Similarity. In: Proceedings of the 8th Workshop on TextGraphs held in conjunction with EMNLP 2013, p. 6-10, Seattle, WA, USA
• Chris Biemann and Martin Riedl (2013): From Global to Local Similarities: A Graph-Based Contextualization Method using Distributional Thesauri. In: Proceedings of the 8th Workshop on TextGraphs held in conjunction with EMNLP 2013, p. 39-43, Seattle, WA, USA
• Janneke Rauscher, Leonard Swiezinski, Martin Riedl and Chris Biemann (2013): Exploring Cities in Crime: Significant Concordance and Co-occurrence in Quantitative Literary Analysis. In: Proceedings of the Computational Linguistics for Literature Workshop held in conjunction with the NAACL HLT 2013, p.61-71, Atlanta, GA, USA
• Martin Riedl and Chris Biemann (2012): Sweeping through the Topic Space: Bad luck? Roll again!. In: Proceedings of the ROBUS-UNSUP 2012: Joint Workshop on Unsupervised and Semi-Supervised Learning in NLP held in conjunction with EACL 2012, p. 19-27, Avignon, France
• Holger Storf, Martin Becker and Martin Riedl (2009): Rule-based Activity Recognition Framework: Challenges, Technique and Learning. In: Proceedings of the 1st PervaSense Workshop held in conjunction with Pervasive Health 2009, p. 1-7, London, England