Alan Akbik

HU Berlin, Machine Learning

Alan focuses on research in machine learning (ML) and natural language processing (NLP), with the goal of giving machines the ability to understand and use human language. This spans research topics such as neural language modeling, sample-efficient learning and semantic parsing, as well as application areas in large-scale text analytics. Together with his group and the open source community, he develops the NLP framework Flair ( that allows anyone to use state-of-the-art NLP methods in their research or applications.

At SCIoI, Alan works at Project A002, Project 44, and Project 45.


SCIoI Publications:

Ziletti, A., Akbik, A., Berns, C., Herold, T., Legler, M., & Viell, M. (2022). Medical Coding with Biomedical Transformer Ensembles and Zero/Few-shot Learning. NAACL, 176–187.
Milich, M., & Akbik, A. (2023). ZELDA: A Comprehensive Benchmark for Supervised Entity Disambiguation. EACL 2023.
Golde, J., Alt, C., & Akbik, A. (2023). TART: Zero- and Few-Shot Named Entity Recognition Across Languages and Domains.
Aynetdinov, A., Alt, C., & Akbik, A. (2023). Parameter-Efficient Tuning Under Weak Supervision.