Herr Dr.

Roman Klinger

Akademischer Oberrat
Institut für Maschinelle Sprachverarbeitung
Theoretische Computerlinguistik

Kontakt

+49 711 685-81406

Pfaffenwaldring 5 b
70569 Stuttgart
Deutschland
Raum: 01.007

Sprechstunde

Only on appointment. For very short appointments, send a mail at least two days in advance. For longer appointments, plan with approximately two weeks leadtime.

Pulications:
  1. 2019

    1. Barnes, J., & Klinger, R. (2019). Embedding Projection for Targeted Cross-Lingual Sentiment: Model Comparisons and a Real-World Study. Journal of Artificial Intelligence Research.
    2. Kim, E., & Klinger, R. (2019b). An Analysis of Emotion Communication Channels in Fan-Fiction: Towards Emotional Storytelling. Proceedings of the Second Workshop of Storytelling. Association for Computational Linguistics.
    3. Ehrlicher, H., Klinger, R., Lehmann, J., & Padó, S. (2019). Measuring Historical Emotions and Their Evolution: An Interdisciplinary Endeavour to Investigate The  `Emotions of Encounter’. Laboratório Interdisciplinar Sobre Informa\,Cão e Conhecimento Em Revista (Liinc Em Revista), 15(1). https://doi.org/10.18617/liinc.v15i1.4557
    4. Bostan, L. A. M., & Klinger, R. (2019). Exploring Fine-Tuned Embeddings that Model Intensifiers for Emotion Analysis. Proceedings of the 10th Workshop on Computational                  Approaches to Subjectivity, Sentiment and Social                  Media Analysis. Presented at the Minneapolis, USA. Minneapolis, USA: Association for Computational Linguistics.
    5. Troiano, E., Padó, S., & Klinger, R. (2019). Crowdsourcing and Validating Event-focused Emotion Corpora for German and English. Proceedings of the Annual Conference of the Association for Computational Linguistics. Presented at the Florence, Italy. Florence, Italy: Association for Computational Linguistics.
    6. Kim, E., & Klinger, R. (2019a). Frowning Frodo, Wincing Leia, and a Seriously Great Friendship: Learning to Classify Emotional Relationships of Fictional Characters. Proceedings of the Annual Conference of the North American Chapter of the Association for Computational Linguistics. Presented at the Minneapolis, USA. Retrieved from http://www.romanklinger.de/publications/KimKlingerNAACL2019.pdf
    7. McHardy, R., Adel, H., & Klinger, R. (2019). Adversarial Training for Satire Detection: Controlling for Confounding Variables. Proceedings of the Annual Conference of the North American Chapter of the Association for Computational Linguistics. Presented at the Minneapolis, USA. Retrieved from http://www.romanklinger.de/publications/McHardyAdelKlinger-NAACL2019.pdf
  2. 2018

    1. Strohm, F., & Klinger, R. (2018). An Empirical Analysis of the Role of Amplifiers, Downtoners, and Negations in Emotion Classification in Microblogs. The 5th IEEE International Conference on Data Science and Advanced Analytics, Special Track on Sentiment, Emotion, and Credibility of Information in Social Data. Presented at the Turin, Italy. https://doi.org/10.1109/DSAA.2018.00087
    2. Klinger, R., de Clercq, O., Mohammad, S. M., & Balahur, A. (2018). IEST: WASSA-2018 Implicit Emotions Shared Task. Proceedings of the 9th Workshop on Computational                  Approaches to Subjectivity, Sentiment and Social                  Media Analysis. Presented at the Brussels, Belgium. Retrieved from http://aclweb.org/anthology/W18-6206
    3. Adel, H., Bostan, L. A. M., Papay, S., Padó, S., & Klinger., R. (2018). DERE: A task and domain-independent slot filling framework for declarative relation extraction. Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing: System Demonstrations. Presented at the Brussels, Belgium. Retrieved from http://aclweb.org/anthology/D18-2008
    4. Barnes, J., Klinger, R., & Schulte im Walde, S. (2018b). Projecting Embeddings for Domain Adaptation: Joint Modeling of Sentiment Analysis in Diverse Domains. Proceedings of COLING 2018, the 27th International Conference on Computational Linguistics. Presented at the Santa Fe, USA. Retrieved from http://www.romanklinger.de/publications/BarnesKlingerSIW2018blsedomain.pdf
    5. Kim, J., & Klinger, R. (2018). Who Feels What and Why? An Annotated Corpus of Modern Literature of Semantic Roles in Emotions. Proceedings of COLING 2018, the 27th International Conference on Computational Linguistics. Presented at the Santa Fe, USA. Retrieved from http://www.romanklinger.de/publications/kimklinger2018reman.pdf
    6. Bostan, L. A. M., & Klinger, R. (2018). A Survey on Annotated Data Sets for Emotion Classification in Text. Proceedings of COLING 2018, the 27th International Conference on Computational Linguistics. Presented at the Santa Fe, USA. Retrieved from http://www.romanklinger.de/publications/BostanKlinger2018coling.pdf
    7. Hartung, M., ter Horst, H., Grimm, F., Diekmann, T., Klinger, R., & Cimiano, P. (2018). SANTO: A Web-based Annotation Tool for Ontology-driven Slot Filling. Proceedings of ACL 2018, System Demonstrations. Presented at the Melbourne, Australia. Melbourne, Australia: Association for Computational Linguistics.
    8. Barnes, J., Klinger, R., & Schulte im Walde, S. (2018a). Bilingual Sentiment Embeddings: Joint Projection of Sentiment Across Languages. Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics. Presented at the Melbourne, Australia. Retrieved from http://www.romanklinger.de/publications/barnes-et-al-2018.pdf
    9. Kicherer, H., Dittrich, M., Grebe, L., Scheible, C., & Klinger, R. (2018). What You Use, Not What You Do: Automatic Classification and Similarity Detection of Recipes. Data and Knowledge Engineering. Retrieved from https://www.sciencedirect.com/science/article/pii/S0169023X17305402
    10. Ter Horst, H., Hartung, M., Klinger, R., Brazda, N., Müller, H. W., & Cimiano, P. (2018). Assessing the Impact of Single and Pairwise Slot                  Constraints in a Factor Graph Model for                  Template-based Information Extraction. Natural Language Processing and Information Systems:                  23rd International Conference on Applications of                  Natural Language to Information Systems, NLDB 2018,                  Paris, France, June 13-15, 2018, Proceedings. Presented at the Cham. Cham: Springer International Publishing.
    11. Thorne, C., & Klinger, R. (2018). On the Semantic Similarity of Disease Mentions in MEDLINE and Twitter. Natural Language Processing and Information Systems: 23rd International Conference on Applications of                  Natural Language to Information Systems, NLDB 2018,                  Paris, France, June 13-15, 2018, Proceedings. Presented at the Cham. Cham: Springer International Publishing.
    12. Barth, F., Kim, E., Murr, S., & Klinger, R. (2018). A Reporting Tool for Relational Visualization and Analysis of Character Mentions in Literature. Book of Abstracts -- Digital Humanities Im Deutschsprachigen Raum. Presented at the Cologne, Germany. Retrieved from http://www.romanklinger.de/publications/BarthKimMurrKlinger2018.html
    13. Braun, M., Klinger, R., Padó, S., & Viehhauser, G. (2018). Digitale Modellierung von Figurenkomplexität am Beispiel des Parzival von Wolfram von Eschenbach. Book of Abstracts -- Digital Humanities Im Deutschsprachigen Raum. Presented at the Cologne, Germany. Retrieved from http://www.romanklinger.de/publications/BraunKlingerPadoViehhauser2018.html
  3. 2017

    1. ter Horst, H., Hartung, M., Klinger, R., Zwick, M., & Cimiano, P. (2017). Predicting Disease-Gene Associations using                  Cross-Document Graph-based Features. ArXiv E-Prints. Retrieved from https://arxiv.org/abs/1709.09239
    2. Thorne, C., & Klinger, R. (2017). Towards Confidence Estimation for Typed Protein-Protein Relation Extraction. Proceedings of the Biomedical NLP Workshop Associated with RANLP. Presented at the Varna, Bulgaria. Retrieved from http://www.romanklinger.de/publications/Thorne2017.pdf
    3. Reiter, N., Schulz, S., Kremer, G., Klinger, R., Viehhauser, G., & Kuhn, J. (2017). Teaching Computational Aspects in the Digital Humanities  Program at University of Stuttgart -- Intentions and  Experiences. Proceedings of the GSCL Workshop on Teaching NLP for Digital Humanities (Teach4DH 2017). Retrieved from http://ceur-ws.org/Vol-1918/reiter.pdf
    4. Brazda, N., ter Horst, H., Hartung, M., Wiljes, C., Estrada, V., Klinger, R., … Cimiano, P. (2017). SCIO: An Ontology to Support the Formalization of Pre-Clinical Spinal Cord Injury Experiments. Workshop on Ontologies and Data in Life Sciences (ODLS 2017), Joint Workshops on Ontologies (JOWO). Presented at the Bolzano, Italy. Retrieved from https://pub.uni-bielefeld.de/download/2913603/2913881
    5. Barnes, J., Klinger, R., & Schulte im Walde, S. (2017). Assessing State-of-the-Art Sentiment Models on State-of-the-Art Sentiment Datasets. Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social                  Media Analysis. Presented at the Copenhagen, Denmark. Copenhagen, Denmark: Association for Computational Linguistics.
    6. Schuff, H., Barnes, J., Mohme, J., Padó, S., & Klinger, R. (2017). Annotation, Modelling and Analysis of Fine-Grained Emotions on a Stance and Sentiment Detection Corpus. Proceedings of the 8th Workshop on Computational                  Approaches to Subjectivity, Sentiment and Social                  Media Analysis. Presented at the Copenhagen, Denmark. Retrieved from http://aclweb.org/anthology/W/W17/W17-5203.pdf
    7. Hartung, M., Klinger, R., Mohme, J., Vogel, L., & Schmidtke, F. (2017). Ranking Right-Wing Extremist Social Media Profiles by Similarity to Democratic and Extremist Groups. Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis. Presented at the Copenhagen, Denmark. Retrieved from http://aclweb.org/anthology/W/W17/W17-5204.pdf
    8. Köper, M., Kim, E., & Klinger, R. (2017). IMS at EmoInt-2017: Emotion Intensity Prediction                  with Affective Norms, Automatically Extended                  Resources and Deep Learning. Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social                  Media Analysis. Presented at the Copenhagen, Denmark. Retrieved from http://aclweb.org/anthology/W/W17/W17-5206.pdf
    9. Kim, E., Padó, S., & Klinger, R. (2017b). Investigating the Relationship between Literary Genres and Emotional Plot Development. Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature (LaTeCH-CLfL). Association for Computational Linguistics.
    10. Kicherer, H., Dittrich, M., Grebe, L., Scheible, C., & Klinger, R. (2017). What You Use, Not What You Do: Automatic Classification of Recipes. In F. Frasincar, A. Ittoo, L. M. Nguyen, & E. Métais (Eds.), Natural Language Processing and Information Systems: 22nd International Conference on Applications of Natural Language to Information Systems, NLDB 2017, Liège, Belgium, June 21-23, 2017, Proceedings (pp. 197--209; By F. Frasincar, A. Ittoo, L. M. Nguyen, & E. Métais). Retrieved from http://dx.doi.org/10.1007/978-3-319-59569-6_22
    11. Klinger, R. (2017). Does Optical Character Recognition and Caption Generation Improve Emotion Detection in Microblog Posts? In F. Frasincar, A. Ittoo, L. M. Nguyen, & E. Métais (Eds.), Natural Language Processing and Information Systems: 22nd International Conference on Applications of Natural Language to Information Systems, NLDB 2017, Liège, Belgium, June 21-23, 2017 (pp. 313--319; By F. Frasincar, A. Ittoo, L. M. Nguyen, & E. Métais). Retrieved from http://dx.doi.org/10.1007/978-3-319-59569-6_39
    12. Sänger, M., Leser, U., & Klinger, R. (2017). Fine-grained Opinion Mining from Mobile App Reviews with Word Embedding Features. In F. Frasincar, A. Ittoo, L. M. Nguyen, & E. Métais (Eds.), Natural Language Processing and Information Systems: 22nd International Conference on Applications of Natural Language to Information Systems, NLDB 2017, Liège, Belgium, June 21-23, 2017, Proceedings (pp. 3--14; By F. Frasincar, A. Ittoo, L. M. Nguyen, & E. Métais). Retrieved from http://dx.doi.org/10.1007/978-3-319-59569-6_1
    13. Hartung, M., Klinger, R., Schmidtke, F., & Vogel, L. (2017). Identifying Right-Wing Extremism in German Twitter Profiles: a Classification Approach. In F. Frasincar, A. Ittoo, L. M. Nguyen, & E. Métais (Eds.), Natural Language Processing and Information Systems: 22nd International Conference on Applications of Natural Language to Information Systems, NLDB 2017, Liège, Belgium, June 21-23, 2017, Proceedings (pp. 320--325; By F. Frasincar, A. Ittoo, L. M. Nguyen, & E. Métais). Retrieved from http://dx.doi.org/10.1007/978-3-319-59569-6_40
    14. Kim, E., Padó, S., & Klinger, R. (2017a). Prototypical Emotion Developments in Literary Genres. Digital Humanities 2017: Conference Abstracts. Presented at the Montréal, Canada. Montréal, Canada: McGill University and Université de Montréal.
  4. 2016

    1. Ling, J., & Klinger, R. (2016). An Empirical, Quantitative Analysis of the Differences Between Sarcasm and Irony. In H. Sack, G. Rizzo, N. Steinmetz, D. Mladenić, S. Auer, & C. Lange (Eds.), The Semantic Web: ESWC 2016 Satellite Events,                  Heraklion, Crete, Greece, May 29 -- June 2, 2016,                  Revised Selected Papers (pp. 203--216; By H. Sack, G. Rizzo, N. Steinmetz, D. Mladenić, S. Auer, & C. Lange). Retrieved from http://dx.doi.org/10.1007/978-3-319-47602-5_39
    2. Scheible, C., Klinger, R., & Padó, S. (2016). Model Architectures for Quotation Detection. Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 1736--1745. Retrieved from http://www.aclweb.org/anthology/P16-1164
    3. Sänger, M., Leser, U., Kemmerer, S., Adolphs, P., & Klinger, R. (2016). SCARE ― The Sentiment Corpus of App Reviews with Fine-grained Annotations in German. In N. Calzolari, K. Choukri, T. Declerck, S. Goggi, M. Grobelnik, B. Maegaard, … S. Piperidis (Eds.), Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC 2016) (By N. Calzolari, K. Choukri, T. Declerck, S. Goggi, M. Grobelnik, B. Maegaard, … S. Piperidis). Retrieved from http://www.lrec-conf.org/proceedings/lrec2016/summaries/59.html
    4. Klinger, R., Suliya, S. S., & Reiter, N. (2016). Automatic Emotion Detection for Quantitative Literary Studies -- A case study based on Franz Kafka’s ``Das Schloss’’ and ``Amerika’’. Digital Humanities 2016: Conference Abstracts, 826--828. Retrieved from http://dh2016.adho.org/abstracts/318
    5. Jaskolski, J., Siegberg, F., Tibroni, T., Cimiano, P., & Klinger, R. (2016). Opinion Mining in Online Reviews About Distance Education Programs. Retrieved from http://arxiv.org/abs/1607.06299
  5. 2015

    1. Kessler, W., Klinger, R., & Kuhn, J. (2015). Towards Opinion Mining from Reviews for the Prediction of Product Rankings. Proceedings of the 6th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, 51--57. Retrieved from http://aclweb.org/anthology/W15-2908
    2. Stöckel, A., Paassen, B., Dickfelder, R., Göpfert, J. P., Brazda, N., Müller, H. W., … Klinger, R. (2015). SCIE: Information Extraction for Spinal Cord Injury Preclinical Experiments – A Webservice and Open Source Toolkit. BioRxiv. Retrieved from http://dx.doi.org/10.1101/013458
  6. 2014

    1. Bagewadi, S., Bobić, T., Hofmann-Apitius, M., Fluck, J., & Klinger, R. (2014). Detecting miRNA Mentions and Relations in Biomedical Literature version  3; referees: 2 approved, 1 approved with reservations. F1000Research, 3(205). Retrieved from http://dx.doi.org/10.12688/f1000research.4591.3
    2. Paassen, B., Stöckel, A., Dickfelder, R., Göpfert, J. P., Brazda, N., Kirchhoffer, T., … Cimiano, P. (2014). Ontology-based Extraction of Structured Information from Publications on Preclinical Experiments for Spinal Cord Injury Treatments. Proceedings of the Third Workshop on Semantic Web and Information Extraction, 25–32. Retrieved from http://www.aclweb.org/anthology/W14-6204
    3. Hartung, M., Klinger, R., Zwick, M., & Cimiano, P. (2014). Towards Gene Recognition from Rare and Ambiguous Abbreviations using a Filtering Approach. Proceedings of BioNLP 2014, 118–127. Retrieved from http://www.aclweb.org/anthology/W14-3418
    4. Ruppenhofer, J., Klinger, R., Struß, J. M., Sonntag, J., & Wiegand, M. (2014). IGGSA Shared Tasks on German Sentiment Analysis. In G. Faaß & J. Ruppenhofer (Eds.), Workshop Proceedings of the 12th Edition of the KONVENS Conference (By G. Faaß & J. Ruppenhofer). Retrieved from http://opus.bsz-bw.de/ubhi/volltexte/2014/319/pdf/04_01.pdf
    5. Buschmeier, K., Cimiano, P., & Klinger, R. (2014). An Impact Analysis of Features in a Classification Approach to Irony Detection in Product Reviews. Proceedings of the 5th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, 42–49. Retrieved from http://www.aclweb.org/anthology/W14-2608
    6. Klinger, R., & Cimiano, P. (2014). The USAGE review corpus for fine grained multi lingual opinion analysis. In N. Calzolari, K. Choukri, T. Declerck, H. Loftsson, B. Maegaard, J. Mariani, … S. Piperidis (Eds.), Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC’14) (pp. 2211–2218; By N. Calzolari, K. Choukri, T. Declerck, H. Loftsson, B. Maegaard, J. Mariani, … S. Piperidis). Retrieved from http://www.lrec-conf.org/proceedings/lrec2014/pdf/85_Paper.pdf
  7. 2013

    1. McCrae, J. P., Cimiano, P., & Klinger, R. (2013). Orthonormal Explicit Topic Analysis for Cross-Lingual Document Matching. Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing, 1732–1740. Retrieved from http://www.aclweb.org/anthology/D13-1179
    2. Bobic, T., & Klinger, R. (2013). Committee-based Selection of Weakly Labeled Instances for Learning Relation Extraction. Research in Computing Science, 70, 187–197. Retrieved from http://www.micai.org/rcs/2013_70/Committee-based%20Selection%20of%20Weakly%20Labeled%20Instances%20for%20Learning%20Relation%20Extraction.html
    3. Klinger, R., & Cimiano, P. (2013a). Bi-directional Inter-dependencies of Subjective Expressions and Targets and their Value for a Joint Model. Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), 848–854. Retrieved from http://www.aclweb.org/anthology/P13-2147
    4. Klinger, R., & Cimiano, P. (2013b). Joint and Pipeline Probabilistic Models for Fine-Grained Sentiment  Analysis: Extracting Aspects, Subjective Phrases and their Relations. 2013 IEEE 13th International Conference on Data Mining Workshops (ICDMW), 937–944. Retrieved from http://dx.doi.org/10.1109/ICDMW.2013.13
  8. 2012

    1. Thomas, P., Bobić, T., Leser, U., Hofmann-Apitius, M., & Klinger, R. (2012). Weakly Labeled Corpora as Silver Standard for Drug-Drug and Protein-Protein Interaction. Proceedings of the Workshop on Building and Evaluating Resources for Biomedical Text Mining (BioTxtM) on Language Resources and Evaluation Conference (LREC). Presented at the Istanbul, Turkey. Retrieved from http://www.romanklinger.de/publications/ppi-ddi.pdf
    2. Bobic, T., Klinger, R., Thomas, P., & Hofmann-Apitius, M. (2012). Improving Distantly Supervised Extraction of Drug-Drug and Protein-Protein Interactions. Proceedings of the Joint Workshop on Unsupervised and Semi-Supervised Learning in NLP, 35–43. Retrieved from http://www.aclweb.org/anthology/W12-0705
    3. Klinger, R., Senger, P., Madan, S., & Jacovi, M. (2012). Online Communities Support Policy-Making: The Need for Data Analysis. In E. Tambouris, A. Macintosh, & Ø. Sæbø (Eds.), Electronic Participation (pp. 132–143; By E. Tambouris, A. Macintosh, & Ø. Sæbø). Retrieved from http://dx.doi.org/10.1007/978-3-642-33250-0_12
    4. Aisopos, F., Kardara, M., Senger, P., Klinger, R., Papaoikonomou, A., Tserpes, K., … Varvarigou, T. A. (2012). E-Government and Policy Simulation in Intelligent Virtual Environments. In K.-H. Krempels & J. Cordeiro (Eds.), WEBIST (pp. 129–135; By K.-H. Krempels & J. Cordeiro). Retrieved from http://dblp.uni-trier.de/db/conf/webist/webist2012.html#AisoposKSKPTGV12
  9. 2011

    1. Klinger, R., Riedel, S., & McCallum, A. (2011). Inter-Event Dependencies support Event Extraction from Biomedical Literature. Mining Complex Entities from Network and Biomedical Data (MIND), European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD). Retrieved from http://www.romanklinger.de/publications/klinger11interevent.pdf
    2. Klinger, R. (2011b). Conditional Random Fields for Named Entity Recognition - Feature Selection and Optimization in Biology and Chemistry. In Fraunhofer Series in Information and Communication Technology. Retrieved from http://www.shaker.de/de/content/catalogue/index.asp?lang=de&ID=8&ISBN=978-3-8440-0213-3
    3. Klinger, R. (2011a). Automatically Selected Skip Edges in Conditional Random Fields for Named Entity Recognition. Proceedings of the International Conference Recent Advances in Natural Language Processing 2011, 580–585. Retrieved from http://aclweb.org/anthology/R11-1082
    4. Thomas, P., Solt, I., Klinger, R., & Leser, U. (2011). Learning Protein Protein Interaction Extraction using Distant Supervision. Proceedings of Workshop on Robust Unsupervised and Semisupervised Methods in Natural Language Processing, 25–32. Retrieved from http://www.aclweb.org/anthology/W11-3904
    5. Thomas, P. E., Klinger, R., Furlong, L. I., Hofmann-Apitius, M., & Friedrich, C. M. (2011). Challenges in the association of human single nucleotide polymorphism mentions with unique database identifiers. BMC Bioinformatics, 12(Suppl 4)(S4). Retrieved from http://dx.doi.org/10.1186/1471-2105-12-S4-S4
  10. 2010

    1. Gurulingappa, H., Klinger, R., Hofmann-Apitius, M., & Fluck, J. (2010). An Empirical Evaluation of Resources for the Identification of Diseases and Adverse Effects in Biomedical Literature. 2nd Workshop on Building and Evaluating Resources for Biomedical Text Mining (7th Edition of the Language Resources and Evaluation Conference). Presented at the Valetta, Malta. Retrieved from http://www.nactem.ac.uk/biotxtm/papers/Gurulingappa.pdf
    2. Müller, B., Klinger, R., Gurulingappa, H., Mevissen, H.-T., Hofmann-Apitius, M., Fluck, J., & Friedrich, C. M. (2010). Abstracts versus Full Texts and Patents: A Quantitative Analysis of Biomedical Entities. Proceedings of the 1st IRF Conference. Retrieved from http://link.springer.com/chapter/10.1007/978-3-642-13084-7_12
  11. 2009

    1. Kolářik, C., Klinger, R., & Hofmann-Apitius, M. (2009). Identification of Histone Modifications in Biomedical Text for Supporting Epigenomic Research. BMC Bioinformatics, 10(S28). Retrieved from http://dx.doi.org/10.1186/1471-2105-10-S1-S28
    2. Klinger, R., & Friedrich, C. M. (2009). Feature Subset Selection in Conditional Random Fields for Named Entity Recognition. In G. Angelova, K. Bontcheva, R. Mitkov, N. Nicolov, & N. Nikolov (Eds.), Proceedings of Recent Advances in Natural Language Processing (RANLP) (pp. 185–191; By G. Angelova, K. Bontcheva, R. Mitkov, N. Nicolov, & N. Nikolov). Retrieved from http://aclweb.org/anthology-new/R/R09/R09-1035.pdf
    3. Gurulingappa, H., Müller, B., Klinger, R., Mevissen, H.-T., Hofmann-Apitius, M., Fluck, J., & Friedrich, C. M. (2009). Patent Retrieval in Chemistry based on semantically tagged Named Entities. In E. M. Voorhees & L. P. Buckland (Eds.), The Eighteenth Text RETrieval Conference (TREC 2009) Proceedings (By E. M. Voorhees & L. P. Buckland). Retrieved from http://trec.nist.gov/pubs/trec18/papers/scai.CHEM.pdf
    4. Risselada, R., Friedrich, C. M., Ebeling, C., Klinger, R., Bauer-Mehren, A., Pastor, M., … Hofmann-Apitius, M. (2009). Workflows for Data Mining in Integrated multi-modal Data of Intracranial Aneurysms using KNIME. Book of Abstracts of the R User Conference (UseR!), 165. Retrieved from http://math.agrocampus-ouest.fr/infoglueDeliverLive/digitalAssets/20008_Risselada_Friedrich_Ebeling_Klinger.pdf
  12. 2008

    1. Smith, L., Tanabe, L. K., nee Ando, R. J., Juo, C.-J., Chung, I.-F., Hsu, C.-N., … Wilbur, W. J. (2008). Overview of BioCreative II Gene Mention Recognition. Genome Biology, 9(Suppl 2), S2.2-S2.18. https://doi.org/10.1186/gb-2008-9-s2-s2
    2. Hofmann-Apitius, M., Fluck, J., Furlong, L., Fornes, O., Kolárik, C., Hanser, S., … Friedrich, C. (2008). Knowledge Environments Representing Molecular Entities for the Virtual Physiological Human. Philosophical Transactions of the Royal Society A. Retrieved from http://dx.doi.org/10.1098/rsta.2008.0099
    3. Kolarik, C., Klinger, R., Friedrich, C. M., Hofmann-Apitius, M., & Fluck, J. (2008). Chemical Names: Terminological Resources and Corpora Annotation. Workshop on Building and Evaluating Resources for Biomedical Text Mining (6th Edition of the Language Resources and Evaluation Conference), 51–58. Marrakech, Morocco.
    4. Klinger, R., Kolářik, C., Fluck, J., Hofmann-Apitius, M., & Friedrich, C. M. (2008). Detection of IUPAC and IUPAC-like Chemical Names. Bioinformatics, 24(13), i268–i276. Retrieved from http://dx.doi.org/10.1093/bioinformatics/btn181
  13. 2007

    1. Klinger, R., & Tomanek, K. (2007). Classical Probabilistic Models and Conditional Random Fields (No. TR07-2–013). Retrieved from Department of Computer Science, Dortmund University of Technology website: https://ls11-www.cs.uni-dortmund.de/_media/techreports/tr07-13.pdf
    2. Klinger, R., Friedrich, C. M., Fluck, J., & Hofmann-Apitius, M. (2007). Named Entity Recognition with Combinations of Conditional Random Fields. Proceedings of the Second BioCreative Challenge Evaluation Workshop, 89–91. Madrid, Spain.
    3. Klinger, R., Furlong, L. I., Friedrich, C. M., Mevissen, H. T., Fluck, J., Sanz, F., & Hofmann-Apitius, M. (2007). Identifying Gene Specific Variations in Biomedical Text. Journal of Bioinformatics and Computational Biology, 5(6), 1277–1296. Retrieved from http://dx.doi.org/10.1142/S0219720007003156
  14. 2005

    1. Bützken, M., Edelkamp, S., Elalaoui, A., Kahl, K., Karmouni, R., Klinger, R., … Wiggers, A. (2005). An Integrated Toolkit for Modern Action Planning. 19th Workshop on New Results in Planning, Scheduling and Design (PUK), 1–11. Retrieved from http://www.puk-workshop.de/puk2005/paper/1_puk1.pdf
  • Information Extraction and Retrieval
  • Sentiment and Opinion Mining
  • Natural Language Processing
  • Biomedical NLP, Bioinformatics
  • Supervised and semi-supervised machine learning methods
  • Graphical Models
  • Data Mining/Text Mining
  • Evolutionary Computation
  • Computational Intelligence/Artificial Intelligence
  • Automatic Composition of Music

For help with finding a topic, please read this website.

  • If you are interested in writing your thesis under my supervision in one of the following topics, please contact me:
    • Information Extraction
    • Sentiment Analysis
    • Bio-medical natural language processing
    • Web-Mining
    • Ontologies
    • Machine Learning
    • Statistical Methods
  • Examples for supervised theses in the past:
    • A machine learning approach to sentiment analysis for distance education evaluation 
    • Ranked Protein-Disease Relations Using Machine-Learning
    • Entwicklung eines Systems zur Erkennung von Ironie in Text
    • Joint Extraction of Proteins and Bio-Molecular Events using Imperatively Defined Factor Graphs
    • Auswirkung des aktiven Lernens auf die Merkmalsselektion bei Textklassikation
    • Twitter als Wahlindikator
    • Stimmungs- und Themenerkennung in politischen Debatten
    • Automated extraction of variation mentions from literature sources and mapping to a unique database identifier

Website with more information: http://www.romanklinger.de/

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