http://www.julielab.de

Visit us on GitHub

https://www.uni-jena.de

© 2019 JULIE Lab

JULIE Lab

About | News | Projects | Staff | Students | Resources | Jobs | Contact | Imprint
About | News | Projects | Staff | Students | Resources | Jobs | Contact | Imprint

[Overview] | [Research] | Publications

Based on the Guide2Research ranking of German computer scientists, I am ranked on position 132 (world ranking: 2884), with (at that time) 9,450 citations. This is the best ranking of all computer scientists from Friedrich-Schiller-Universität Jena.

Prof. Dr. Udo Hahn’s publications

Google Scholar | Researchgate | Semantic Scholar | ACL Anthology | DBPL

1) All-time Favorites
2) Natural Language Processing: Systems & Applications
2a) Information Extraction & Text Mining
2b) Text Summarization
2c) Knowledge Base Population
2d) (Multilingual) Information Retrieval
3) Natural Language Processing Infrastructure
3a) Language Resources: Gene/Protein & Biomedical Corpora
3b) Language Resources: Medical & Clinical Corpora
3c) Language Resources: Email Corpora
3d) Language Resources: Other Corpora
3e) Language Resources: Software, Tools & Frameworks (including UIMA)
3f) Corpus Annotation
4) Natural Language Processing: Methods
4a) Parsing & Semantic Interpretation
4b) Anaphora Resolution & Discourse Structure Analysis
4c) Emotion and Sentiment Analysis
5) NLP for the Life Sciences
5a) Biomedical NLP
5b) Clinical NLP
5c) Term Extraction
5d) Bioinformatics & Clinical Applications
6) NLP for Economics
7) Knowledge Representation and Reasoning
8) Biomedical Ontology Engineering
9) Machine Learning
10) Digital Humanities

1) All-time Favorites

M. Strube & U. Hahn [1999].
Functional centering: grounding referential coherence in information structure. In Computational Linguistics, 25(3):309-344.

K. Markert & U. Hahn [2002].
Understanding metonymies in discourse. In Artificial Intelligence, 135(1-2):145-198.

U. Hahn & I. Mani [2000].
The challenges of automatic summarization. In IEEE Computer, 33(11):29-36.

U. Hahn, K.B. Cohen, Y. Garten & N. Shah [2012].
Mining the pharmacogenomics literature: a survey of the state of the art. In Briefings in Bioinformatics, 13(4):460-494.

S. Schulz & U. Hahn [2005].
Part-whole representation and reasoning in formal biomedical ontologies. In Artificial Intelligence in Medicine, 34(3):179-200.

J. Wermter, K. Tomanek & U. Hahn [2009].
High-performance gene name normalization with GeNo. In Bioinformatics, 25(6):815-821.

U. Hahn, S.Schulz & M. Romacker [1999].
Part-whole reasoning: a case study in medical ontology engineering. In IEEE Intelligent Systems, 14(5):59-67.

E. Buyko, E. Faessler, J. Wermter & U. Hahn [2011].
Syntactic simplification and semantic enrichment: trimming dependency graphs for event extraction. In Computational Intelligence, 27(4):610-644.

U. Hahn, J. Wermter, R. Blasczyk & P. Horn [2007].
Text mining: powering the database revolution. In Nature, 448(7150):130.

C. Fellbaum, U. Hahn & B. Smith [2006].
Towards new information resources for public health: from WordNet to Medical WordNet. In Journal of Biomedical Informatics, 39(3):321-332.

U. Hahn, M. Jarke & Th. Rose [1991].
Teamwork support in a knowledge-based information systems environment. In IEEE Transactions on Software Engineering, SE-17(5):467-482.

U. Hahn [1990].
Topic parsing: accounting for text macro structures in full-text analysis. In Information Processing & Management, 26(1):135-170.

U. Hahn, S. Schacht & N. Bröker [1994].
Concurrent, object-oriented natural language parsing: the ParseTalk model. In International Journal of Human-Computer Studies, 41(1-2):179-222.

U. Hahn [1989].
Making understanders out of parsers: semantically driven parsing as a key concept for realistic text understanding applications. In International Journal of Intelligent Systems, 4(3):345-393.

2) Natural Language Processing: Systems & Applications

2a) Information Extraction & Text Mining

E. Buyko, E. Faessler, J. Wermter & U. Hahn [2011].
Syntactic simplification and semantic enrichment: trimming dependency graphs for event extraction. In Computational Intelligence, 27(4):610-644.

J. Wermter, K. Tomanek & U. Hahn [2009].
High-performance gene name normalization with GeNo. In Bioinformatics, 25(6):815-821.

U. Hahn, & M. Oleynik [2020].
Medical information extraction in the age of deep learning. In Yearbook of Medical Informatics 2020 – Ethics in Health Informatics. IMIA & Georg Thieme Verlag KG, pp. 208-20

Y. Kano, J. Bjorne, F. Ginter, T. Salakoski, E. Buyko, U. Hahn, K.B. Cohen, K. Verspoor, C. Roeder, L.E. Hunter, H. Kilicoglu, S. Bergler, S. van Landeghem, T. van Parys, Y. van de Peer, M. Miwa, S. Ananiadou, M. Neves, A. Pascual-Montano, A. Ozgur, D.R. Radev, S. Riedel, R. Saetre, H.W. Chun, J.D. Kim, S. Pyysalo, T. Ohta, & J’i. Tsujii [2011].
U-Compare bio-event meta-service: compatible BioNLP event extraction services. In BMC Bioinformatics, 12(481).

E. Buyko & U. Hahn [2010].
Evaluating the impact of alternative dependency graph encodings on solving event extraction tasks. In EMNLP 2010 – Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing. M.I.T., Boston, MA, USA, October 9-11, 2010, pp. 982-992.

U. Hahn & K. Schnattinger [1997].
Deep knowledge discovery from natural language texts. In KDD’97 – Proceedings of the 3rd International Conference on Knowledge Discovery and Data Mining. Newport Beach, CA, August 14-17, 1997. AAAI Press, pp. 175-178.

E. Buyko, E. Beisswanger & U. Hahn [2012].
Extraction of pharmacogenetic and pharmacogenomic relations: a case study using PharmGKB. In PSB 2012 – Proceedings of the Pacific Symposium on Biocomputing 2012. Big Island, HI, USA, January 3-7, 2012, pp. 376-387.

E. Buyko & U. Hahn [2011].
Generating semantics for the life sciences via text analytics. In ICSC 2011 – Proceedings of the 5th IEEE International Conference on Semantic Computing. Stanford, CA, USA, September 18-21, 2011. IEEE Computer Society Press, pp. 193-196

D. Rebholz-Schuhmann, S. Clematide, F. Rinaldi, S. Kafkas, E.M. van Mulligen, C. Bui, J. Hell-rich, I. Lewin, D. Milward, M. Poprat, A. Jimeno-Yepes, U. Hahn & J. A. Kors. [2013].
Entity recognition in parallel multi-lingual biomedical corpora: The CLEF-ER laboratory overview. In Information Access Evaluation: Multilinguality, Multimodality, and Visualization. CLEF 2013 – Proceedings of the 4th International Conference of the CLEF Initiative. Valencia, Spain, September 23-26, 2013. Springer, pp.353-367 (Lecture Notes in Computer Science, 8138).

E. Buyko, J. Linde, S. Priebe & U. Hahn [2011].\ Towards automatic pathway generation from biological full-text publications. In Advances in Intelligent Data Analysis X. IDA 2011 – Proceedings of the 10th International Conference on Intelligent Data Analysis. Porto, Portugal, October 29-31, 2011. Springer, pp. 67-79 (Lecture Notes in Computer Science, 7014)

2b) Text Summarization

U. Hahn & I. Mani [2000]. The challenges of automatic summarization. In IEEE Computer, 33(11):29-36.

U. Hahn & U. Reimer [1999].
Knowledge-based text summarization: Salience and generalization operators for knowledge base abstraction. In I. Mani & M. T. Maybury (Eds.), Advances in Automatic Text Summarization. MIT Press, pp. 215-232.

U. Hahn & U. Reimer [1998]. Text summarization based on terminological logics. In ECAI ‘98 – Proceedings of the 13th European Conference on Artificial Intelligence. Brighton, U.K., August 23-28, 1998. J. Wiley, pp. 165-169.

U. Reimer & U. Hahn [1988]. Text condensation as knowledge base abstraction. In CAIA ‘88 – Proceedings of the 4th IEEE/AAAI Conference on Artificial Intelligence Applications. San Diego, CA, USA, March 14-18, 1988. Computer Society Press of the IEEE, pp. 338-344.

U. Hahn & U. Reimer [1986]. TOPIC essentials. In COLING ‘86 – Proceedings of the 11th International Conference on Computational Linguistics. Bonn, Germany, 25-29 August 1986, pp. 497-503.

U. Hahn & U. Reimer [1984]. Computing text constituency: An algorithmic approach to the generation of text graphs. In Research and Development in Information Retrieval. SIGIR ‘84 – Proceedings of the 3rd Joint BCS and ACM Symposium. Cambridge, England, 2-6 July 1984. Cambridge University Press, pp. 343-368.

2c) Knowledge Base Population

U. Hahn, M. Romacker & S. Schulz [2000]. Content management in the SynDiKATe system: How technical documents are automatically transformed to text knowledge bases. In Data and Knowledge Engineering, 35(2):137-159.

U. Hahn & K. Schnattinger [1998]. Towards text knowledge engineering. In AAAI ‘98 – Proceedings of the 15th National Conference on Artificial Intelligence. Madison, WI, USA, July 26-30, 1998. AAAI Press - MIT Press, pp. 524-531.

U. Hahn & K. Schnattinger [1997]. Knowledge mining from textual sources. In CIKM ‘97 – Proceedings of the 6th International Conference on Information and Knowledge Management. Las Vegas, NV, USA, November 10-14, 1997, pp. 83-90.

U. Hahn & M. Romacker [2001]. The SynDiKATe text knowledge base generator. In HLT 2001 – Proceedings of the 1st International Conference on Human Language Technology Research. San Diego, CA, USA, March 18-21, 2001, pp. 328-333.

U. Hahn, M. Romacker & S. Schulz [2002]. Creating knowledge repositories from biomedical reports: The medSynDiKATe text mining system. In PSB 2002 – Pacific Symposium on Biocomputing. Kauai, Hawaii, USA, January 3-7, 2002, pp.338-349.

2d) (Multilingual) Information Retrieval (SeMedico, MorphoSaurus)

E. Faessler, M. Oleynik, & U. Hahn [2020].
What makes a top-performing precision medicine search engine? Tracing main system features in a systematic way. In SIGIR ‘20 – Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. July 25–30, 2020 (Virtual Event), pp. 459-468.

E. Faessler, M. Oleynik, & U. Hahn, Udo [2019].
JULIE Lab & Med Uni Graz @ TREC 2019 Precision Medicine Track. In TREC 2019 – Proceedings of the 28th Text REtrieval Conference. Gaithersburg, Maryland, USA, November 13-15, 2019 [top-ranked system: best system in 5/6 tasks/measurements & 2nd best system in 1/6 tasks/measurements]

E. Faessler & U. Hahn [2017].
SeMedico: A comprehensive semantic search engine for the life sciences. In ACL 2017 – Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics: System Demonstrations. Vancouver, B.C., Canada, August 1, 2017, pp. 91-96.

K. Markó, S. Schulz, A. Medelyan & U. Hahn [2005].
Boostrapping dictionaries for cross-language information retrieval. In SIGIR 2005 – Proceedings of the 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. Salvador, Brazil, August 15-19, 2005. ACM Press, pp. 528-535.

U. Hahn, K. Markó & S. Schulz [2004].
Learning indexing patterns from one language for the benefit of others. In AAAI ‘04 – Proceedings of the 19th National Conference on Artificial Intelligence. San José, CA, July 25-29, 2004. AAAI Press - MIT Press, pp. 406-411.

K. Markó, S. Schulz & U. Hahn [2005].
MorphoSaurus: design and evaluation of an interlingua-based, cross-language document retrieval engine for the medical domain. In Methods of Information in Medicine, 44(4):537-545.

S. Schulz & U. Hahn [2000].
Morpheme-based, cross-lingual indexing for medical document retrieval. In International Journal of Medical Informatics, 59(3):87-99.

3) Natural Language Processing Infrastructure

3a) Language Resources: Gene/Protein & Biomedical Corpora

D. Rebholz-Schuhmann, A.J.J. Yepes, E.M. van Mulligen, N. Kang, J. Kors, D. Milward, P. Corbett, E. Buyko, E. Beisswanger & U. Hahn [2010].
The CALBC silver standard corpus: Harmonizing multiple semantic annotations in a large biomedical corpus. In Journal of Bioinformatics and Computational Biology, 8(1):163-179

E. Faessler, L. Modersohn, C. Lohr, & U. Hahn [2020].
ProGene : a large-scale, high-quality protein-gene annotated benchmark corpus. In LREC 2020 – Proceedings of the 12th International Conference on Language Resources and Evaluation. Marseille, France, May 11-16, 2020, pp. 4585-4596.

E. Buyko, E. Beisswanger, & U. Hahn [2010].
The GeneReg corpus for gene expression regulation events: an overview of the corpus and its in-domain and out-of-domain interoperability. In LREC 2010 – Proceedings of the 7th International Conference on Language Resources and Evaluation. La Valletta, Malta, May 17-23, 2010, pp. 2662-2666.

U. Hahn, K. Tomanek, E. Beisswanger, & E. Faessler [2010].
A proposal for a configurable silver standard. In LAW IV – Proceedings of the 4th Linguistic Annotation Workshop @ ACL 2010. Uppsala, Sweden, July 15-16, 2010, pp. 235-242.

J. Hellrich, S. Clematide, U. Hahn & D. Rebholz-Schuhmann [2014].
Collaboratively annotating multilingual parallel corpora in the biomedical domain: some MANTRAs. In LREC 2014 – Proceedings of the 9th Language Resources and Evaluation Conference. Reykjavik, Iceland, 26-31 May, 2014, pp. 4033-4040.

Ş. Kafka, I. Lewin, D. Milward, E. van Mulligen, J. Kors, U. Hahn & D. Rebholz-Schuhmann [2012].
CALBC: releasing the final corpora. In LREC 2012 – Proceedings of the 8th International Conference on Language Resources and Evaluation. Istanbul, Turkey, May 21-27, 2012, pp. 2923-2926.

U. Hahn, E. Beisswanger, E. Buyko, M. Poprat & J. Wermter [2008].
Semantic annotations for biology: A corpus development initiative at the Jena University Language & Information Engineering (JULIE) Lab. In LREC 2008 – Proceedings of the 6th International Conference on Language Resources and Evaluation. Marrakech, Morocco, 26-31 May 2008, pp. 2257-2261.

D. Rebholz-Schuhmann, A. Jimeno, C. Li, S. Kafkas, I. Lewin, N. Kang, P. Corbett, D. Milward, E. Buyko, E. Beisswanger, K. Hornbostel, A. Kouznetsov, R. Witte, J. B. Laurila, C .J. O. Baker, C.-J. Kuo, S. Clematide, F. Rinaldi, R. Farkas, G. Móra, K. Hara, L. I. Furlong, M. Rautschka, M. L. Neves, A. Pascual-Montano, Q. Wei, N. Collier, F. M. Chowdhury, A. Lavelli, R. Berlanga, R. Morante, V. van Asch, W. Daelemans, J. L. Marina, E. van Mulligen, J. Kors, & U. Hahn [2011].
Assessment of NER Solutions against the First and Second CALBC Silver Standard Corpus. In Journal of Biomedical Semantics, Vol. 2(Suppl 5):S11

3b) Language Resources: Medical & Clinical Corpora

C. Lohr, S. Buechel & U. Hahn [2018].
Sharing copies of synthetic clinical corpora without physical distribution: a case study to get around IPRs and privacy constraints featuring the German JSynCC corpus. In LREC 2018 – Proceedings of the 11th International Conference on Language Resources and Evaluation. Miyazaki, Japan, May 7-12, 2018, pp. 1259-1266.

U. Hahn, E. Beisswanger, E. Buyko, & E. Faessler [2012].
Active learning-based corpus annotation: the PathoJen experience. In AMIA 2012 – Proceedings of the 36th Annual Symposium of the American Medical Informatics Association. Informatics. Chicago, IL, USA, November 3-7, 2012, pp. 301-310.

U. Hahn, F. Matthies, C. Lohr, & M. Löffler [2018].
3000PA : towards a national reference corpus of German clinical language. In MIE 2018 – Proceedings of Medical Informatics Europe 2018. Gothenburg, Sweden, 24-26 April 2018. IOS Press, pp. 26-30 (Studies in Health Technology and Informatics, Vol. 247).

F. Borchert, C. Lohr, L. Modersohn, Th. Langer, M. Follmann, J.P. Sachs, U. Hahn, & M.-P. Schapranow [2020].
GGPONC : a corpus of German medical text with rich metadata based on clinical practice guidelines. In LOUHI 2020 – Proceedings of the 11th International Workshop on Health Text Mining and Information Analysis @ EMNLP 2020. November 20, 2020 (Virtual Event) pp. 38–48.

J. Wermter & U. Hahn [2004].
An annotated German-language medical text corpus as language resource. In LREC 2004 – Proceedings of the 4th International Conference on Language Resources and Evaluation. Vol. 2, Lisbon, Portugal, 26-28 May 2004. pp. 473-476.

U. Hahn, E. Beisswanger, E. Buyko, E. Faessler, J. Traumüller, S. Schröder & K. Hornbostel [2012].
Iterative refinement and quality checking of annotation guidelines: how to deal effectively with semantically sloppy named entity types, such as pathological phenomena. In LREC 2012 – Proceedings of the 8th International Conference on Language Resources and Evaluation. Istanbul, Turkey, May 21-27, 2012, pp. 3881-3885.

C. Lohr, St. Luther, F. Matthies, L. Modersohn, D. Ammon, K. Saleh, A. Henkel, M. & Kiehntopf, & U. Hahn, Udo [2018].
CDA-compliant section annotation of German-language discharge summaries: guideline development, annotation campaign, section classification. In AMIA 2018 – Proceedings of the 2018 Annual Symposium of the American Medical Informatics Association. Data, Technology, and Innovation for Better Health. San Francisco, California, USA, November 3-7, 2018, pp. 770–779.

3c) Language Resources: Email Corpora

E. Eder, U. Krieg-Holz, & U. Hahn [2020].
CodE Alltag 2.0 : a pseudonymized German-language email corpus. In LREC 2020 – Proceedings of the 12th International Conference on Language Resources and Evaluation. Marseille, France, May 11-16, 2020, pp. 4466-4477.

U. Krieg-Holz, C. Schuschnig, F. Matthies, B. Redling & U. Hahn [2016].
CodE Alltag : a German-language e-mail corpus. In LREC 2016 – Proceedings of the 10th International Conference on Language Resources and Evaluation. Portorož, Slovenia, 23-28 May 2016, pp. 2543-2550.

E. Eder, U. Krieg-Holz, & U. Hahn, Udo [2019].
De-identification of emails: pseudonymizing privacy-sensitive data in a German email corpus. In RANLP 2019 – Proceedings of the 12th International Conference on “Recent Advances in Natural Language Processing:” Natural Language Processing in a Deep Learning World. Varna, Bulgaria, 2–4 September, 2019, pp. 259–269.

3d) Language Resources: Other Corpora

U. Hahn, & T. Duan [2019].
Corpus assembly as text data integration from digital libraries and the Web. In JCDL ‘19 – Proceedings of the 19th ACM/IEEE-CS Joint Conference on Digital Libraries. Urbana-Champaign, Illinois, USA, June 2-6, 2019, pp. 25-28.

S.G.M. Händschke, S. Buechel, J. Goldenstein, Ph. Poschmann , T. Duan, P. Walgenbach, & U. Hahn [2018].
A corpus of corporate annual and social responsibility reports: 280 million tokens of balanced organizational writing. In ECONLP 2018 – Proceedings of the 1st Workshop on Economics and Natural Language Processing @ ACL 2018. Melbourne, Victoria, Australia, July 20, 2018, pp. 20-31.

3e) Language Resources: Software, Tools & Frameworks (including UIMA)

A. Winter, S. Stäubert, D. Ammon, S. Aiche, O. Beyan, V. Bischoff, Ph. Daumke, St. Decker, G. Funkat, J.E. Gewehr, A. de Greiff, S. Haferkamp, U. Hahn, A. Henkel, T. Kirsten, Th. Klöss, J. Lippert, M. Löbe, V. Lowitsch, O. Maassen, J. Maschmann, S. Meister, R. Mikolajczyk, M. Nüchter, M.W. Pletz, E. Rahm, M. Riedel, K. Saleh, A. Schuppert, St. Smers, A. Stollenwerk, St. Uhlig, Th. Wendt, S. Zenker, W. Fleig, G. Marx, A. Scherag, & M. Löffler [2018].
Smart Medical Information Technology for Healthcare (SMITH). Data integration based on interoperability standards. In Methods of Information in Medicine, 57, e92-e105.

U. Hahn, F. Matthies, E. Faessler & J. Hellrich [2016].
UIMA-based JCoRe 2.0 goes GitHub and Maven Central: State-of-the-art software resource engineering and distribution of NLP pipelines. In LREC 2016 – Proceedings of the 10th International Conference on Language Resources and Evaluation. Portorož, Slovenia, 23-28 May 2016, pp. 2502-2509.

J. Hellrich, S. Buechel, & U. Hahn [2018].
JeSemE : a website for exploring diachronic changes in word meaning and emotion. In COLING 2018 – Proceedings of the 27th International Conference on Computational Linguistics: System Demonstrations. Santa Fe, New Mexico, USA, August 21 & 23, 2018, pp. 10–14.

C. Lohr, J. Kiesel, St. Luther, J. Hellrich, B. Stein, & U. Hahn [2019].
Continuous annotation quality control, support for hierarchically structured label sets and long-segment annotation with WAT-SL 2.0. In LAW XIII 2019 – Proceedings of the 13th Linguistic Annotation Workshop @ ACL 2019. Florence, Italy, August 1, 2019, pp. 215–219.

K. Tomanek, J. Wermter & U. Hahn [2007].
Sentence and token splitting based on conditional random fields. In PACLING 2007 – Proceedings of the 10th Conference of the Pacific Association for Computational Linguistics. Melbourne, Australia, September 19-21, 2007, pp. 49-57.

E. Buyko, K. Tomanek & U. Hahn [2007].
Resolution of coordination ellipses in biological named entities using conditional random fields. In PACLING 2007 – Proceedings of the 10th Conference of the Pacific Association for Computational Linguistics. Melbourne, Australia, September 19-21, 2007, pp. 163-171.

E. Buyko & U. Hahn [2008].
Fully embedded type systems for the semantic annotation layer. In ICGL 2008 – Proceedings of the 1st International Conference on Global Interoperability for Language Resources. Hong Kong, SAR, January 9-11, 2008, pp. 26-33.

E. Beisswanger & U. Hahn [2010].
JULIE Lab’s UIMA Collection Reader for Wikipedia. In Proceedings of the LREC 2010 Workshop on New Challenges for NLP Frameworks. La Valletta, Malta, May 22, 2010, pp. 15-19.

E. Faessler, R. Landefeld, K. Tomanek & U. Hahn [2009].
LuCas: a Lucene CAS indexer. In C. Chiarcos, R. E. de Castilho & M. Stede (Eds.). From Form to Meaning: Processing Texts Automatically. Proceedings of the Biennial GSCL Conference 2009. Potsdam, Deutschland, October 1, 2009. Gunter Narr, pp. 217-224.

3f) Corpus Annotation

K. Tomanek, U. Hahn, S. Lohmann & J. Ziegler [2010].
A cognitive cost model of annotations based on eye-tracking data. In ACL 2010 – Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics. Uppsala, Sweden, July 11-16, 2010, pp. 1158-1167.

E. Faessler, & U. Hahn, Udo [2018].
Annotation data management with JeDIS. In DocEng ‘18 – Proceedings of the 18th ACM Symposium on Document Engineering 2018. Halifax, Nova Scotia, Canada, August 28-31, 2018, #42.

E. Faessler, J. Hellrich & U. Hahn [2014].
Disclose models, hide the data: how to make use of confidential corpora without seeing sensitive raw data. In LREC 2014 – Proceedings of the 9th Language Resources and Evaluation Conference. Reykjavik, Iceland, 26-31 May, 2014, pp. 4230-4237.

K. Tomanek & U. Hahn [2010].
Annotation time stamps: temporal metadata from the linguistic annotation process. In LREC 2010 – Proceedings of the 7th International Conference on Language Resources and Evaluation. La Valletta, Malta, May 17-23, 2010, pp. 2516-2521.

S. Buechel & U. Hahn [2017].
Readers vs. writers vs. texts: coping with different perspectives of text understanding in emotion annotation. In: LAW XI 2017 – Proceedings of the 11th Linguistic Annotation Workshop @ EACL 2017. Valencia, Spain, April 3, 2017, pp. 1-12.

4) Natural Language Processing: Methods

4a) Parsing & Semantic Interpretation

M. Romacker, K. Markert & U. Hahn [1999]. Lean semantic interpretation. In IJCAI ‘99 – Proceedings of the 16th International Joint Conference on Artificial Intelligence. Vol. 2. Stockholm, Sweden, July 31 - August 6, 1999. Morgan Kaufmann, pp. 868-875.

K. Markó, S. Schulz & U. Hahn [2005]. Unsupervised multilingual word sense disambiguation via an interlingua. In AAAI ‘05 – Proceedings of the 20 >th National Conference on Artificial Intelligence. Pittsburgh, PA, July 9-13, 2005. MIT Press, pp. 1075-1080.

U. Hahn, N. Bröker & P. Neuhaus [2000]. Let’s ParseTalk: Message-passing protocols for object-oriented parsing. In H. Bunt & A. Nijholt (Eds.), Advances in Probabilistic and Other Parsing Technologies. Kluwer, pp. 177-201 (Text, Speech and Language Technology Series, 16).

U. Hahn & G. Adriaens [1994]. Parallel natural language processing: Background and overview. In G. Adriaens & U. Hahn (Eds.), Parallel Natural Language Processing. Ablex, pp. 1-134.

E. Buyko & U. Hahn [2008]. Are morpho-syntactic features more predictive for the resolution of noun phrase coordination ambiguity than lexico-semantic similarity scores? In COLING 2008 – Proceedings of the 22nd International Conference on Computational Linguistics. Manchester, England, August 18-22, 2008, pp. 89-96.

U. Hahn & C. Engelmann [2014]. Grounding epistemic modality in speakers’ judgments. In Trends in Artificial Intelligence. PRICAI 2014 – Proceedings of the 13th Pacific Rim International Conference on Artificial Intelligence. Gold Coast, Australia, 1-5 December, 2014. Springer, pp. 654–667 (Lecture Notes in Artificial Intelligence, 8862).

M. Romacker & U. Hahn [2000]. An empirical assessment of semantic interpretation. In ANLP-NAACL 2000 – Proceedings of the 6th Applied Natural Language Processing Conference & the 1st Conference of the North American Chapter of the Association for Computational Linguistics. Seattle, WA, USA, April 29 - May 4, 2000. Morgan Kaufmann, pp. 327-334.

C. Engelmann & U. Hahn [2014]. An empirically grounded approach to extend the linguistic coverage and lexical diversity of verbal probabilities. In CogSci 2014 – Proceedings of the 36th Annual Cognitive Science Conference. Québec City, Québec, Canada, July 23-26, 2014, pp. 451-456.

U. Hahn & M. Romacker [2000]. An integrated model of semantic and conceptual interpretation from dependency structures. In COLING 2000 – Proceedings of the 18 >th International Conference on Computational Linguistics. Saarbrücken, Germany, July 31 - August 4, 2000. Morgan Kaufmann, pp. 271-277.

U. Hahn [1986]. A generalized word expert model of lexically distributed text parsing. In: B. du Boulay, D. Hogg & L. Steels (Eds.), Advances in Artificial Intelligence - II. ECAI ‘86 – 7th European Conference on Artificial Intelligence. Brighton, U.K., July 20-25, 1986. North-Holland, pp. 417-425.

U. Hahn [1989]. Making understanders out of parsers: Semantically driven parsing as a key concept for realistic text understanding applications. In International Journal of Intelligent Systems, 4(3):345-393.

U. Hahn [1994]. An actor model of distributed natural language parsing. In G. Adriaens & U. Hahn (Eds.), Parallel Natural Language Processing. Ablex, pp. 307-349.

U. Hahn, S. Schacht & N. Bröker [1994]. Concurrent, object-oriented natural language parsing: The ParseTalk model. In International Journal of Human-Computer Studies, 41(1-2):179-222.

U. Hahn [1989]. Making understanders out of parsers: Semantically driven parsing as a key concept for realistic text understanding applications. In International Journal of Intelligent Systems, 4(3):345-393

4b) Anaphora Resolution & Discourse Structure Analysis

M. Strube & U. Hahn [1999]. Functional centering: Grounding referential coherence in information structure. In Computational Linguistics, 25(3):309-344.

K. Markert & U. Hahn [2002]. Understanding metonymies in discourse. In Artificial Intelligence, 135(1-2):145-198.

U. Hahn [1990]. Topic parsing: Accounting for text macro structures in full-text analysis In Information Processing & Management, 26(1):135-170

M. Strube & U. Hahn [1996]. Functional centering.
In ACL ‘96 – Proceedings of the 34th Annual Meeting of the Association for Computational Linguistics. Santa Cruz, CA, USA, 24-27 June 1996. Morgan Kaufmann, pp. 270-277.

K. Markert & U. Hahn [1997]. On the interaction of metonymies and anaphora. In IJCAI ‘97 – Proceedings of the 15th International Joint Conference on Artificial Intelligence. Vol. 2. Nagoya, Japan, August 23-29, 1997. Morgan Kaufmann, pp. 1010-1015.

U. Hahn, K. Markert & M. Strube [1996]. A conceptual reasoning approach to textual ellipsis. In ECAI ‘96 – Proceedings of the 12th European Conference on Artificial Intelligence. Budapest, Hungary, August 11-16, 1996. J. Wiley, pp. 572-576.

U. Hahn & M. Strube [1997]. Centering in-the-large: Computing referential discourse segments. In ACL-EACL ‘97 – Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics & 8th Conference of the European Chapter of the Association for Computational Linguistics. Madrid, Spain, July 7-12, 1997. Morgan Kaufmann, pp. 104-111.

M. Strube & U. Hahn [1995]. ParseTalk about sentence- and text-level anaphora. In EACL ´95 – Proceedings of the 7th Conference of the European Chapter of the Association for Computational Linguistics. Dublin, Ireland, March 27-31, 1995, pp. 237-244.

4c) Emotion and Sentiment Analysis

S. Buechel, S. Rücker, & U. Hahn [2020].
Learning and evaluating emotion lexicons for 91 languages. In ACL 2020 – Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. July 5-10, 2020 (Virtual Event), pp. 1202-1217.

S. Buechel & U. Hahn, Udo [2018].
Word emotion induction for multiple languages as a deep multi-task learning problem. In NAACL-HLT 2018 – Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Volume 1: Long Papers. New Orleans, LA, USA, June 1-6, 2018, pp. 1907–1918.

S. Buechel & U. Hahn [2016].
Emotion analysis as a regression problem: Dimensional models and their implications on emotion representation and metrical evaluation. In ECAI 2016 – Proceedings of the 22nd European Conference on Artificial Intelligence. Part 2: ECAI Long Papers. The Hague, Netherlands, August 29 - September 2, 2016. IOS Press, pp. 1114-1122 (Frontiers in Artificial Intelligence and Applications, 285)

S. Buechel & U. Hahn [2017].
EmoBank : studying the impact of annotation perspective and representation format on dimensional emotion analysis. In EACL 2017 – Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics. Vol. 2: Short Papers. Valencia, Spain, April 3-7, 2017, pp. 578–585.

S. Buechel, & U. Hahn [2018].
Emotion representation mapping for automatic lexicon construction (mostly) performs on human level. In COLING 2018 – Proceedings of the 27th International Conference on Computational Linguistics: Main Conference. Santa Fe, New Mexico, USA, August 20-26, 2018, pp. 2892–2904.

S. Buechel & U. Hahn [2017].
A flexible mapping scheme for discrete and dimensional emotion representations. In CogSci 2017 – Proceedings of the 39th Annual Meeting of the Cognitive Science Society. London, UK, 26-29 July 2017, pp. 180-185.

S. Buechel & U. Hahn [2018].
Representation mapping: a novel approach to generate high-quality multi-lingual emotion lexicons. In LREC 2018 – Proceedings of the 11th International Conference on Language Resources and Evaluation. Miyazaki, Japan, May 7-12, 2018, pp. 184-191.

J. Hellrich, S. Buechel, & U. Hahn [2019].
Modeling word emotion in historical language: quantity beats supposed stability in seed word selection. In LaTeCH-CLfL 2019 – Proceedings of the 3rd Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature @ NAACL-HLT 2019. Minneapolis, Minnesota, USA, June 7, 2019, pp. 1–11.

E. Eder, U. Krieg-Holz, & U. Hahn [2019].
At the lower end of language: exploring the vulgar and obscene side of German. In ALW 3 – Proceedings of the 3rd Workshop on Abusive Language Online @ ACL 2019. Florence, Italy, August, 1, 2019, pp. 119–128

5) NLP for the Life Sciences

5a) Biomedical NLP

U. Hahn, K.B. Cohen, Y. Garten & N. Shah [2012].
Mining the pharmacogenomics literature: a survey of the state of the art. In Briefings in Bioinformatics, 13(4):460-494.

J. Wermter, K. Tomanek & U. Hahn [2009].
High-performance gene name normalization with GeNo. In Bioinformatics, 25(6):815-821

Y. Kano, J. Bjorne, F. Ginter, T. Salakoski, E. Buyko, U. Hahn, K.B. Cohen, K. Verspoor, C. Roeder, L.E. Hunter, H. Kilicoglu, S. Bergler, S. van Landeghem, T. van Parys, Y. van de Peer, M. Miwa, S. Ananiadou, M. Neves, A. Pascual-Montano, A. Ozgur, D.R. Radev, S. Riedel, R. Saetre, H.W. Chun, J.D. Kim, S. Pyysalo, T. Ohta, & J’i. Tsujii 2011 [2011].
U-Compare bio-event meta-service: Compatible BioNLP event extraction services. In BMC Bioinformatics, 12(481).

U. Hahn, J. Wermter, R. Blasczyk & P. Horn [2007].
Text mining: powering the database revolution. In Nature, 448(7150):130

F. Matthies & U. Hahn [2017].
Scholarly information extraction is going to make a quantum leap with PubMed Central (PMC)® – but moving from abstracts to full texts seems harder than expected. In MedInfo 2017 – Proceedings of the 16th World Congress on Medical and Health Informatics. Hangzhou, China, 21-25 August 2017. IOS Press, pp. 521-525 (Studies in Health Technology and Informatics, 245)

J. Wermter & U. Hahn [2004].
Really, is medical sublanguage that different? Experimental counter-evidence from tagging medical and newspaper corpora. In MedInfo 2004 – Proceedings of the 11th World Congress on Medical Informatics. Vol. 1. San Francisco, CA, USA, September 7-11, 2004. IOS Press, pp. 560-564 (Studies in Health Technology and Informatics, 107).

U. Hahn, M. Romacker & S. Schulz [1999].
Discourse structures in medical reports: Watch out! The generation of referentially coherent and valid text knowledge bases in the medSynDiKATe system. In International Journal of Medical Informatics, 53(1):1-28.

U. Hahn & J. Wermter [2004].
High-performance tagging on medical texts. In COLING 2004 – Proceedings of the 20th International Conference on Computational Linguistics. Vol.2. Geneva, Switzerland, August 23-27, 2004, pp.973-979.

U. Hahn, P. Daumke, S. Schulz & K. Markó [2005].
Cross-language mining for acronyms and their completions from the Web. In DS 2005 – Proceedings of the 8th International Conference on Discovery Science. Singapore, 8-11 October 2005. Springer, pp. 113-123 (Lecture Notes in Artificial Intelligence, 3735).

K. Tomanek, J. Wermter & U. Hahn [2007].
A reappraisal of sentence and token splitting for life sciences documents. In MedInfo 2007 – Proceedings of the 12th World Congress on Health (Medical) Informatics. Brisbane, Australia, August 20-24, 2007. IOS Press, pp. 524-528 (Studies in Health Technology and Informatics, 129)

E. Buyko, K. Tomanek & U. Hahn [2007].
Resolution of coordination ellipses in biological named entities using conditional random fields. In PACLING 2007 – Proceedings of the 10th Conference of the Pacific Association for Computational Linguistics. Melbourne, Australia, September 19-21, 2007, pp. 163-171.

M. Poprat, K. Markó & U. Hahn [2006].
A language classifier that automatically divides medical documents for experts and health care consumers. In MIE 2006 – Proceedings of the 20th International Congress of the European Federation for Medical Informatics. Maastricht, Netherlands, 27-30 August 2006. IOS Press, pp. 503-508 (Studies in Health Technology and Informatics, 124).

5b) Clinical NLP

U. Hahn, M. Romacker & S. Schulz [2002].
medSynDiKATe: a natural language system for the extraction of medical information from findings reports. In International Journal of Medical Informatics, 67(1-3):63-74.

T. Kolditz, C. Lohr, J. Hellrich, L. Modersohn, B. Betz, M. Kiehntopf, & U. Hahn [2019].
Annotating German clinical documents for de-identification. In MEDINFO 2019 – Proceedings of the 17th World Congress on Medical and Health Informatics: Health and Wellbeing e-Networks for All. Lyon, France, 25-30 August 2019, pp. 203-207.

J. Hellrich, F. Matthies, E. Faessler & U. Hahn [2015].
Sharing models and tools for processing German clinical texts. In MIE 2015 – Proceedings of the 26th Medical Informatics in Europe Conference. Madrid, Spain, May 27-29, 2015. IOS Press, pp. 734-738 (Studies in Health Technology and Informatics, 210).

5c) Term Extraction

J. Wermter & U. Hahn [2006].
You can’t beat frequency (unless you use linguistic knowledge): A qualitative evaluation of association measures for collocation and term extraction. In COLING-ACL 2006 – Proceedings of the Joint 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics. Vol. 2. Sydney, Australia, July 17-21, 2006, pp. 785-792.

J. Wermter & U. Hahn [2005].
Paradigmatic modifiability statistics for the extraction of complex multi-word terms. In HLT-EMNLP 2005 – Proceedings of the Human Language Technology Conference and the Conference on Empirical Methods in Natural Language Processing. Vancouver, B. C., Canada, 6-8 October 2005, pp. 843-850.

J. Wermter & U. Hahn [2004].
Collocation extraction based on modifiability statistics. In COLING 2004 – Proceedings of the 20th International Conference on Computational Linguistics. Vol.2. Geneva, Switzerland, August 23-27, 2004, pp. 980-986.

J. Hellrich & U. Hahn [2014].
Fostering multilinguality in the UMLS: a computational approach to terminology expansion for multiple languages. In AMIA 2014 – Proceedings of the Annual Symposium of the American Medical Informatics Association. Washington, D.C., USA, November 15-19, 2014, pp.655-660; 660a-660d.

J. Wermter & U. Hahn [2005].
Finding new terminology in very large corpora. In K-CAP ‘05 – Proceedings of the 3rd International Conference on Knowledge Capture. Banff, Canada, October 2-5, 2005. ACM Press, pp. 137-144.

J. Hellrich, S. Schulz, S. Buechel & U. Hahn [2015].
JUFIT: a configurable rule engine for filtering and generating new multilingual UMLS terms. In AMIA 2015 – Proceedings of the 2015 Annual Symposium of the American Medical Informatics Association. San Francisco, CA, USA, Nov 14-18, 2015, pp. 604-610.

J. Wermter & U. Hahn [2005].
Massive biomedical term discovery. In DS 2005 – Proceedings of the 8 >th International Conference on Discovery Science. Singapore, 8-11 October 2005. Springer, pp. 281-293 (Lecture Notes in Artificial Intelligence, 3735).

J. Hellrich, S. Schulz, S. Buechel & U. Hahn [2015].
Adding multilingual terminological resources to parallel corpora for statistical machine translation deteriorates system performance: a negative result from experiments in the biomedical domain. In Text, Speech, and Dialogue. TSD 2015 – Proceedings of the 18th International Conference on Text, Speech, and Dialogue. Pilsen, Czech Republic, September 14-17, 2015. Springer, pp. 506-514 (Lecture Notes in Artificial Intelligence, 9302).

J. Hellrich & U. Hahn [2014].
Exploiting parallel corpora to scale multilingual biomedical terminologies. In MIE 2014 – Proceedings of the Medical Informatics in Europe Conference. Istanbul, Turkey, August 31 - September 3, 2014. IOS Press, pp. 575-578 (Studies in Health Technology and Informatics, 205).

5d) Bioinformatics and Clinical Applications

A. Sadik, L.F. Somarribas Patterson, S. Öztürk, S.R. Mohapatra, V. Panitz, P.F. Secker, P. Pfänder, S. Loth, H. Salem, M.T. Prentzell, B. Berdel, M. Iskar, E. Faessler, F. Reuter, I. Kirst, V. Kalter, K.I. Foerster, E. Jäger, C. Ramallo Guevara, M. Sobeh, Th. Hielscher, G. Poschet, A. Reinhardt, J.C. Hassel, M. Zapatka, U. Hahn, A. von Deimling, C. Hopf, R. Schlichting, B.I. Escher, J. Burhenne, W. Haefeli, N. Ishaque, A. Böhme, S. Schäuble, K. Thedieck, S. Trump, M. Seiffert, & C.A. Opitz [2020].
IL4|1 is a metabolic immune checkpoint that activates the AHR and promotes tumor progression. In Cell, 182, 1252-1270.e34.

P. Dalle Pezze, S. Ruf, A. G. Sonntag, M. Langelaar-Makkinje, P. Hall, A. M. Heberle, P. Razquin Navas, K. van Eunen, R. C. Tölle, J.J. Schwarz, H. Wiese, B. Warscheid, J. Deitersen, B Stork, E. Fäßler, S. Schäuble, U. Hahn, P. Horvatovich, D. P. Shanley & K. Thedieck [2016].
A systems study reveals concurrent activation of AMPK and mTOR by amino acids. In Nature Communications, 7(13254).

L. Reimann, A.N. Schwäble, A.L. Fricke, W.W.D. Mühlhäuser, Y. Leber, K. Lohanadan, M.G. Puchinger, S.Schäuble, E. Faessler, H. Wiese, Chr. Reichenbach, B. Knapp, C.D. Peikert, F. Drepper, U. Hahn, C. Kreutz, P.F.M. van der Ven, G. Radziwill, K. Djinović-Carugo, D.O. Fürst, & B. Warscheid [2020].
Phosphoproteomics identifies dual-site phosphorylation in an extended basophilic motif regulating FILIP1-mediated degradation of filamin-C. In Communications Biology [Nature], 3, #253.

C. Weber, L. Röschke, L. Modersohn, C. Lohr, T. Kolditz, U. Hahn, D. Ammon, B. Betz, & M. Kiehntopf [2020].
Optimized identification of advanced chronic kidney disease and absence of kidney disease by combining different electronic health data resources and by applying machine learning strategies. In Journal of Clinical Medicine, 9, #2955.

R. Altwasser, J. Linde, E. Buyko, U. Hahn, R. Guthke [2012].
Genome-wide scale-free network inference for Candida albicans. In Frontiers in Microbiology, 3(51).

6) NLP for Economics

S. Buechel, U. Hahn, J. Goldenstein, S. G. M. Händschke & P. Walgenbach [2016].
Do enterprises have emotions? In WASSA 2016 – Proceedings of the 7th Workshop on Computational Approaches to Subjectivity, Sentiment & Social Media Analysis @ NAACL-HLT 2016. San Diego, CA, USA, June 16, 2016, pp. 147-153.

S. Buechel, S. Junker, T. Schlaak, C. Michelsen, & U. Hahn [2019].
A time series analysis of emotional loading in Central Bank statements. In ECONLP 2019 – Proceedings of the 2nd Workshop on Economics and Natural Language Processing @ EMNLP-IJCNLP 2019. Hong Kong, China, November 4, 2019, pp. 16–21.

7) Knowledge Representation and Reasoning

S. Schulz & U. Hahn [2005]. Part-whole representation and reasoning in formal biomedical ontologies. In Artificial Intelligence in Medicine, 34(3):179-200.

S. Schulz, K. Markó & U. Hahn [2007]. Spatial location and its relevance for terminological inferences in bio-ontologies. In BMC Bioinformatics, 8(134)

U. Hahn, S. Schulz & M. Romacker [1999]. Part-whole reasoning: A case study in medical ontology engineering. In IEEE Intelligent Systems, 14(5):59-67.

S. Staab & U. Hahn [1999]. Scalable temporal reasoning. In IJCAI ‘99 – Proceedings of the 16th International Joint Conference on Artificial Intelligence. Vol. 2. Stockholm, Sweden, July 31 - August 6, 1999. Morgan Kaufmann, pp. 1247-1252.

U. Hahn, S. Schulz & M. Romacker [1999]. Partonomic reasoning as taxonomic reasoning in medicine. In AAAI ‘99 – Proceedings of the 16th National Conference on Artificial Intelligence. Orlando, FL, July 18-22,

  1. AAAI Press - MIT Press, pp. 271-276.

U. Hahn, S. Schulz & K. Markó [2004]. Mereological semantics for bio-ontologies. In AAAI ‘04 – Proceedings of the 19th National Conference on Artificial Intelligence. San José, CA, July 25-29, 2004. AAAI Press - MIT Press, pp. 257-262

Staab & U. Hahn [1997]. “Tall”, “good”, “high”: Compared to what? In IJCAI ‘97 – Proceedings of the 15th International Joint Conference on Artificial Intelligence. Vol. 2. Nagoya, Japan, August 23-29, 1997. Morgan Kaufmann, pp. 996-1001.

S. Schulz & U. Hahn [2004]. Parthood as spatial inclusion: Evidence from biomedical conceptualizations. In Principles of Knowledge Representation and Reasoning. KR 2004 – Proceedings of the 9th International Conference. Whistler, B. C., Canada, June 2-5, 2004 AAAI Press, pp. 55-63.

S. Schulz & U. Hahn [2002]. Necessary parts and wholes in bio-ontologies. In Principles of Knowledge Representation and Reasoning. KR 2002 – Proceedings of the 8th International Conference. Toulouse, France, April 22-25, 2002. Morgan Kaufmann, pp. 387-394.

S. Staab & U. Hahn [1997]. Comparatives in context. In: AAAI ‘97 – Proceedings of the 14th National Conference on Artificial Intelligence. Providence, RI, July 27-31, 1997. AAAI Press - MIT Press, pp. 616-621.

S. Schulz, P. Daumke, B. Smith & U. Hahn [2005]. How to distinguish parthood from location in bioontologies. In AMIA 2005 – Proceedings of the Annual Symposium of the American Medical Informatics Association. Washington, D.C., USA, October 22-26, 2005, pp. 669-673.

S. Schulz & U. Hahn [2004]. Representing natural kinds by spatial inclusion and containment. In ECAI 2004 – Proceedings of the 16th European Conference on Artificial Intelligence. Valencia, Spain, August 22-27, 2004. IOS Press, pp. 403-407 (Frontiers in Artificial Intelligence and Applications, 110)

S. Schulz & U. Hahn [2001]. Mereotopological reasoning about parts and (w)holes in bio-ontologies. In Formal Ontology in Information Systems. FOIS 2001 – Collected Papers from the 2nd International Conference. Ogunquit, ME, October 17-19, 2001. ACM Press, pp. 210-221.

U. Reimer & U. Hahn [1985]. On formal semantic properties of a frame data model. In Computers and Artificial Intelligence, 4(4):335-351

U. Reimer & U. Hahn [1983]. A formal approach to the semantics of a frame data model In IJCAI ‘83 – Proceedings of the 8th International Joint Conference on Artificial Intelligence. Vol. 1. Karlsruhe, West Germany, 8-12 August 1983. W. Kaufmann, pp. 337-339.

8) Biomedical Ontology Engineering

S. Schulz & U. Hahn [2007]. Towards the ontological foundations of symbolic biological theories. In Artificial Intelligence in Medicine, 9(3):237-250

D. DeLuca, E. Beisswanger, J. Wermter, P. Horn, U. Hahn & R. Blasczyk [2009]. MaHCO: An ontology of the Major Histocompatibility Complex for immunoinformatic applications and text mining. In Bioinformatics, 25(16):2064-2070.

E. Beisswanger, S. Schulz, H. Stenzhorn & U. Hahn [2008]. BioTop: An upper domain ontology for the life sciences: A description of its current structure, contents, and interfaces to OBO ontologies. In Applied Ontology, 3(4):205-212

U. Hahn & S. Schulz [2004]. Building a very large ontology from medical thesauri. In S. Staab & R. Studer (Eds.), Handbook on Ontologies. Springer, pp. 133-150.

S. Schulz & U. Hahn [2001]. Medical knowledge reengineering: Converting major portions of the UMLS into a terminological knowledge base. In International Journal of Medical Informatics, 64(2-3):207-221.

U. Hahn, M. Romacker & S. Schulz [1999]. How knowledge drives understanding: Matching medical ontologies with the needs of medical language processing. In Artificial Intelligence in Medicine, 5(1):25-51.

S. Schulz & U. Hahn [2000]. Knowledge engineering by large-scale knowledge reuse: Experience from the medical domain. In KR 2000 – Proceedings of the 7th International Conference on Principles of Knowledge Representation and Reasoning. Breckenridge, CO, April 12-15,

  1. Morgan Kaufmann, pp. 601-610.

E. Beisswanger, V. Lee, J.-J. Kim, D. Rebholz-Schuhmann, A. Splendiani, O. Dameron, S. Schulz & U. Hahn [2008]. Gene Regulation Ontology (GRO): Design principles and use cases. In MIE 2008 – Proceedings of the 21st International Congress of the European Federation for Medical Informatics, Gothenborg, Sweden, May 25-28, 2008. IOS Press, pp. 9-14 (Studies in Health Technology and Informatics, 136).

E. Faessler, F. Klan, A. Algergawy, B. König-Ries & U. Hahn [2017]. Selecting and tailoring ontologies with JOYCE. In Knowledge Engineering and Knowledge Management. Revised Selected Papers of the EKAW 2016 Satellite Events, EKM and Drift-an-LOD. Bologna, Italy, November 19–23,

  1. Springer, pp. 114-118 (Lecture Notes in Artificial Intelligence, 10180).

E. Beisswanger, J. Wermter & U. Hahn [2010]. Aligning UniProt and MeSH: A case study on human protein terms. In MedInfo 2010 – Proceedings of the 13th World Congress on Medical Informatics. Cape Town, South Africa, September 12-15, 2010. IOS Press, pp. 1030-1034 (Studies in Health Technology and Informatics, 160)

H. Stenzhorn, S. Schulz, E. Beisswanger, U. Hahn, L. van den Hoek, & E. van Mulligen [2008]. BioTop and ChemTop: Top-domain ontologies for biology and chemistry. In: ISWC-PD ‘08 - Proceedings of the 7th International Semantic Web Conference. Posters and Demonstrations. Karlsruhe, Germany, October 26-30, 2008, pp. 50-51. (CEUR Workshop Proceedings, 401)

U. Hahn & S. Schulz [2002]. Massive bio-ontology engineering for NLP. In HLT 2002 – Proceedings of the 2 >nd International Conference on Human Language Technology Research. San Diego, CA, USA, March 24-27,

  1. Morgan Kaufmann, pp. 68-75.

S. Schulz & U. Hahn [2004]. Ontological foundations of biological continuants. In Formal Ontology in Information Systems. FOIS 2004 – Proceedings of the 3rd International Conference. Turin, Italy, November 4-6, 2004. IOS Press, pp. 319-330 (Frontiers in Artificial Intelligence and Applications, 114)

S. Schulz, E. Beisswanger, U. Hahn, J. Wermter, A. Kumar & H. Stenzhorn [2006]. From GENIA to BioTop : Towards a top-level ontology for biology. In Formal Ontology in Information Systems. FOIS 2006 – Proceedings of the 4th International Conference. Baltimore, MD, USA, November 9-11, 2006. IOS Press, pp. 103-114 (Frontiers in Artificial Intelligence and Applications, 150).

C. Fellbaum, U. Hahn & B. Smith [2006]. Towards new information resources for public health – From WordNet to Medical WordNet. In Journal of Biomedical Informatics, 39(3):321-332.

U. Hahn [2013]. Semantic technologies: A computational paradigm for making sense of qualitative meaning structures. In B.-O. Küppers, U. Hahn & S. Artmann (Eds.) Evolution of Semantic Systems. Springer, pp.151-173.

9) Machine Learning

K. Tomanek & U. Hahn [2009]. Semi-supervised active learning for sequence labeling. In ACL-IJCNLP 2009 – Proceedings of the 47th Annual Meeting of the Association for Computational Linguistics & 4th International Joint Conference on Natural Language Processing of the Asian Federation of Natural Language Processing. Singapore, August 2-7, 2009, pp. 1039-1047.

J. Hellrich & U. Hahn [2016]. Bad company: Neighborhoods in neural embedding spaces considered harmful. In COLING 2016 – Proceedings of the 26th International Conference on Computational Linguistics. Technical Papers. Osaka, Japan, 11-16 December 2016, pp. 2785-2796.

R. Reichart, K. Tomanek, U. Hahn & A. Rappoport [2008].
Multi-task active learning for linguistic annotations. In ACL 2008 – Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies. Columbus, OH, USA, June 15-20, 2008, pp. 861-869.

K. Tomanek, J. Wermter & U. Hahn [2007]. An approach to text corpus construction which cuts annotation costs and maintains reusability of annotated data. In EMNLP-CoNLL 2007 – Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning. Prague, Czech Republic, June 28-30, 2007, pp. 486-495.

K. Tomanek & U. Hahn [2010]. A comparison of models for cost-sensitive active learning. In COLING 2010 – Proceedings of the 23rd International Conference on Computational Linguistics. Beijing, China, August 23-27, 2010, pp. 1247-1255.

K. Tomanek & U. Hahn [2009]. Reducing class imbalance during active learning for named entity annotation. In K-CAP 2009 – Proceedings of the 5th International Conference on Knowledge Capture. Redondo Beach, CA, USA, September 1-4, 2009. ACM Press, pp. 105-112.

K. Schnattinger & U. Hahn [1998]. Quality-based learning. In ECAI ‘98 – Proceedings of the 13th European Conference on Artificial Intelligence. Brighton, U.K., August 23-28, 1998. J. Wiley, pp. 160-164.

M. Klenner & U. Hahn [1994]. Concept versioning: A methodology for tracking evolutionary concept drift in dynamic concept systems. In ECAI ‘94 – Proceedings of the 11 >th European Conference on Artificial Intelligence. Amsterdam, The Netherlands, August 8-12 1994. J. Wiley, pp. 473-477.

U. Hahn & K. Schnattinger [1998]. A text understander that learns. In COLING-ACL ‘98 – Proceedings of the 36 >th Annual Meeting of the Association for Computational Linguistics & 17th International Conference on Computational Linguistics. Vol. 1, Montréal, Québec, Canada, August 10-14, 1998. Morgan Kaufmann, pp. 476-482.

U. Hahn, M. Klenner & K. Schnattinger [1996]. Learning from texts: A terminological meta-reasoning perspective. In S. Wermter, E. Riloff & G. Scheler (Eds.), Connectionist, Statistical and Symbolic Approaches to Learning for Natural Language Processing. Springer, pp.453-468 (Lecture Notes in Artificial Intelligence, 1040).

J. Hellrich, B. Kampe, & U. Hahn [2019].
The influence of down-sampling strategies on SVD word embedding stability. In RepEval 2019 – Proceedings of the 3rd Workshop on Evaluating Vector Space Representations for NLP @ NAACL-HLT 2019. Minneapolis, Minnesota, USA, June 6, 2019, pp. 18–26.

J. Hellrich, S. Buechel, & U. Hahn [2019].
Modeling word emotion in historical language: quantity beats supposed stability in seed word selection. In LaTeCH-CLfL 2019 – Proceedings of the 3rd Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature @ NAACL-HLT 2019. Minneapolis, Minnesota, USA, June 7, 2019, pp. 1–11.

10) Digital Humanities

J. Hellrich & U. Hahn [2017].
Exploring diachronic lexical semantics with JeSemE. In ACL 2017 – Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics: System Demonstrations. Vancouver, B.C., Canada, August 1, 2017, pp. 31-36.

J. Hellrich, S. Buechel, & U. Hahn [2018].
JeSemE : a website for exploring diachronic changes in word meaning and emotion. In COLING 2018 – Proceedings of the 27th International Conference on Computational Linguistics: System Demonstrations. Santa Fe, New Mexico, USA, August 21 & 23, 2018, pp. 10–14.

S. Buechel, J. Hellrich & U. Hahn [2017].
The course of emotion in three centuries of German text: a methodological framework. In Digital Humanities 2017 – Conference Abstracts of the 2017 Conference of the Alliance of Digital Humanities Organizations (ADHO). Montréal, Québec, Canada, August 8-11, 2017.

J. Hellrich, S. Buechel, & U. Hahn [2019].
Modeling word emotion in historical language: quantity beats supposed stability in seed word selection. In LaTeCH-CLfL 2019 – Proceedings of the 3rd Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature @ NAACL-HLT 2019. Minneapolis, Minnesota, USA, June 7, 2019, pp. 1–11.

J. Hellrich & U. Hahn [2016].
Measuring the dynamics of lexico-semantic change since the German Romantic period. In Digital Humanities 2016 – Conference Abstracts of the 2016 Conference of the Alliance of Digital Humanities Organizations (ADHO). Kraków, Poland, 11-16 July 2016, pp. 545-547.

B. Kampe, T. Duan, & U. Hahn [2020].
‘Allgemeine Musikalische Zeitung’ as a searchable online corpus. In LREC 2020 – Proceedings of the 12th International Conference on Language Resources and Evaluation. Marseille, France, May 11-16, 2020, 969-976.

J. Hellrich & U. Hahn [2016].
An assessment of experimental protocols for tracing changes in word semantics relative to accuracy and reliability. In LaTeCH 2016 – Proceedings of the 10th SIGHUM Workshop on Language Technology for Cultural Heritage, Social Sciences, and Humanities @ ACL 2016. Berlin, Germany, August 11, 2016, pp. 111-117.

S. Buechel, J. Hellrich & U. Hahn [2016].
Feelings from the past: adapting affective lexicons for historical emotion analysis. In LT4DH 2016 – Proceedings of the Workshop on Language Technology Resources and Tools for Digital Humanities @ COLING 2016. Osaka, Japan, 11 December 2016, pp. 54-61.


Visit us on GitHub

https://www.uni-jena.de

© 2019 JULIE Lab