Biography

Hi! I am Jan. I just defended my Ph.D. thesis which I conducted at the Ubiquitous Knowledge Processing (UKP) Lab, Technical University of Darmstadt, Germany. My research focuses on data annotation, especially improving annotation efficiency for NLP. As part of the INCEpTION project, we build a batteries-included platform for text annotation. I also am an experienced software engineer, especially in backend development as well as a system architect and administrator. In my free time, I love to learn Mandarin.

In addition to my current research, I am also available as an AI consultant. I have a strong background in data annotation including best practices, increasing annotation efficiency and quality, software backend engineering as well as building AI IT infrastructure. If that sounds interesting, then I would be happy hearing from you!

Interests

Data Annotation
Interactive Machine Learning
Reinforcement Learning
Continual Learning
Information Retrieval
Chinese

Education

PhD in Computer Science, 2018 - 2024
TU Darmstadt
MSc in Computer Science, 2017
TU Darmstadt
BSc in Computer Science, 2014
DHBW Mannheim

Recent Publications

Jan-Christoph Klie, Ji-Ung Lee, Kevin Stowe, Gözde Şahin, Nafise Sadat Moosavi, Luke Bates, Dominic Petrak, Richard Eckart de Castilho, Iryna Gurevych (2023). Lessons Learned from a Citizen Science Project for Natural Language Processing. Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics.

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Jan-Christoph Klie, Bonnie Webber, Iryna Gurevych (2023). Annotation Error Detection: Analyzing the Past and Present for a More Coherent Future. Computational Linguistics.

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Jan-Christoph Klie, Richard Eckart de Castilho, Iryna Gurevych (2023). Analyzing Dataset Annotation Quality Management in the Wild.

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Ji-Ung Lee, Jan-Christoph Klie, Iryna Gurevych (2022). Annotation Curricula to Implicitly Train Non-Expert Annotators. Computational Linguistics.

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Jan-Christoph Klie, Richard Eckart de Castilho, Iryna Gurevych (2020). From Zero to Hero: Human-In-The-Loop Entity Linking in Low Resource Domains. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics.

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