HunFlair: An Easy-to-Use Tool for State-of-the-Art Biomedical Named Entity Recognition
This provides a more accurate and accessible tool for researchers and practitioners in biomedical information extraction, though it is incremental as it builds on existing frameworks.
The authors tackled the problem of biomedical named entity recognition by developing HunFlair, an easy-to-use tool integrated into the Flair framework, which outperforms other state-of-the-art standalone tools with an average gain of 7.26 percentage points.
Summary: Named Entity Recognition (NER) is an important step in biomedical information extraction pipelines. Tools for NER should be easy to use, cover multiple entity types, highly accurate, and robust towards variations in text genre and style. To this end, we propose HunFlair, an NER tagger covering multiple entity types integrated into the widely used NLP framework Flair. HunFlair outperforms other state-of-the-art standalone NER tools with an average gain of 7.26 pp over the next best tool, can be installed with a single command and is applied with only four lines of code. Availability: HunFlair is freely available through the Flair framework under an MIT license: https://github.com/flairNLP/flair and is compatible with all major operating systems. Contact:{weberple,saengema,alan.akbik}@informatik.hu-berlin.de