Nils Reimers

Nils Reimers, M.Sc.

Postdoctoral researcher

+49 6151 16-25296
+49 6151 16-25295

Hochschulstraße 10
64289 Darmstadt

Office: S2|02 B115


Research interests

  • Event and Temporal Relation Extraction
    Millions of news articles are published every day. Finding, extracting and structuring the important information in an efficient way is a crucial task in many domains, for example in algorithmic trading. I do research on the automatic event and relation extraction on news articles. Each event is characterized by the involved entities, by a connecting property like a certain action, and a temporal information.Subsequently, temporal and causal relations are extracted for those events.
  • Deep Learning
    For my research, I apply various super- and semi-supervised machine learning techniques for the event and relation detection. A special research focus lies on deep learning, which allows to model high-level abstraction of data. I use and extend deep learning techniques in order to extract events and relations within documents and across documents.


Currently I work in two projects:

  • DARIAH DE-II: I'm involved in the development of quantitative methods to detect the evolution of narrative techniques in (German) novels over the past centuries.
  • Structuring Story-Chains: As thousands of news articles are published daily, it is challenging to stay up-to-date on every topic. The goal of the project is to help readers to tackle the information-overload. This is done by extracting and analyzing the causal connections between articles. The results are useful for various tasks, e.g. to get a quicker overview on a certain topic.

Awards & grants



Student guidance

I'm supervising Bachelor- and Master-theses in the field of Natural Language Processing in combination with Machine Learning. In case you like to write a thesis in the following fields, feel free to approach me:

  • Structuring Story-Chains: You are interested to analyze thousands of news articles and creating an intelligent system, supporting either readers or editors of news websites? This project has the opportunity for various types of theses: Reading recommendation systems, visualization of large datasets & graphs, information extraction from news articles, link discovery, as well as human-machine-interaction.
  • The Deep Learning Revolution: Deep Learning uses deep neural networks to achieve state-of-the-art results in all domains of machine learning. In my research I investigate different deep learning approach and apply them to text data. In case you are interested to write a thesis in this field, feel free to approach me.

I have supervised the following theses:

  • Philip Beyer. Proposal for a STS Evaluation Framework for STS based Applications. Student Research Paper (Studienarbeit). Computer Science Department. Technical University of Darmstadt. Published at Coling 2016.
  • Ziyang Li. Related Articles Discovery in Large Corpora (Masterthesis). Computer Science Department. Technical University of Darmstadt.
  • Michael Bräunlein. Multi-Document High Precision Event Extraction (Masterthesis). Computer Science Department. Technical University of Darmstadt.

Biographical information

I hold a M.Sc. (Master of Science) in IT-Security from the Technical University of Darmstadt, a B.Sc. (Bachelor of Science) in Computer Science, and a B.Sc. in Mathematics from the University of Oldenburg.


Gruppiere nach: Datum | Typ des Eintrags | Keine Gruppierung
Springe zu: 2018 | 2017 | 2016 | 2015 | 2014 | 2013
Anzahl der Einträge: 12.


Reimers, Nils ; Gurevych, Iryna :
Why Comparing Single Performance Scores Does Not Allow to Draw Conclusions About Machine Learning Approach.
In: arXiv:1803.09578
[Artikel] , (2018)

Reimers, Nils ; Dehghani, Nazanin ; Gurevych, Iryna :
Event Time Extraction with a Decision Tree of Neural Classifiers.
In: Transactions of the Association for Computational Linguistics, 6 pp. 77-89. ISSN 2307-387X
[Artikel] , (2018)


Reimers, Nils ; Gurevych, Iryna :
Reporting Score Distributions Makes a Difference: Performance Study of LSTM-networks for Sequence Tagging.
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing (EMNLP)
[Konferenz- oder Workshop-Beitrag] , (2017)

Reimers, Nils ; Gurevych, Iryna :
Optimal Hyperparameters for Deep LSTM-Networks for Sequence Labeling Tasks.
In: arXiv preprint arXiv:1707.06799
[Artikel] , (2017)


Reimers, Nils ; Beyer, Philip ; Gurevych, Iryna :
Task-Oriented Intrinsic Evaluation of Semantic Textual Similarity.
Proceedings of the 26th International Conference on Computational Linguistics (COLING)
[Konferenz- oder Workshop-Beitrag] , (2016)

Reimers, Nils ; Dehghani, Nazanin ; Gurevych, Iryna :
Temporal Anchoring of Events for the TimeBank Corpus.
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (ACL 2016) Association for Computational Linguistics
[Konferenz- oder Workshop-Beitrag] , (2016)

Reimers, Nils ; Jannidis, Fotis ; Pielström, Steffen ; Pernes, Stefan ; Reger, Isabella :
A Tool for NLP-Preprocessing in Literary Text Analysis.

[Konferenz- oder Workshop-Beitrag] , (2016)

Jannidis, Fotis ; Pernes, Stefan ; Pielström, Steffen ; Reger, Isabella ; Reimers, Nils ; Vitt, Thorsten :
DARIAH-DKPro-Wrapper Output Format (DOF) Specification.

[Report] , (2016)


Reimers, Nils ; Gurevych, Iryna :
Event Nugget Detection, Classification and Coreference Resolution using Deep Neural Networks and Gradient Boosted Decision Trees.
Proceedings of the Eighth Text Analysis Conference (TAC 2015) National Institute of Standards and Technology (NIST)
[Konferenz- oder Workshop-Beitrag] , (2015)


Reimers, Nils ; Eckle-Kohler, Judith ; Schnober, Carsten ; Kim, Jungi ; Gurevych, Iryna
Faaß, Gertrud ; Ruppenhofer, Josef (eds.) :

GermEval-2014: Nested Named Entity Recognition with Neural Networks.
Workshop Proceedings of the 12th Edition of the KONVENS Conference Universitätsverlag Hildesheim
[Konferenz- oder Workshop-Beitrag] , (2014)


Deiseroth, B. ; Fehr, Victoria ; Fischlin, Marc ; Maasz, M. ; Reimers, Nils ; Stein, R. :
Computing on Authenticated Data for Adjustable Predicates.
In: Lecture Notes in Computer Science, 7954 pp. 53-68.
[Artikel] , (2013)

Steinebach, M. ; Klöckner, P. ; Reimers, Nils ; Wienand, D. ; Wolf, Patrick :
Robust Hash Algorithms for Text.
In: Lecture Notes in Computer Science ((tba)).
[Konferenz- oder Workshop-Beitrag] , (2013)

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