S1000: a better taxonomic name corpus for biomedical information extraction

Motivation The recognition of mentions of species names in text is a critically important task for biomedical text mining. While deep learning-based methods have made great advances in many named entity recognition tasks, results for species name recognition remain poor. We hypothesize that this is primarily due to the lack of appropriate corpora.Results We introduce the S1000 corpus, a comprehensive manual re-annotation and extension of the S800 corpus. We demonstrate that S1000 makes highly accurate recognition of species names possible (F-score =93.1%), both for deep learning and dictionary-based methods.Availability and implementationAll resources introduced in this study are available under open licenses from . The webpage contains links to a Zenodo project and three GitHub repositories associated with the study.

Automated summary

The recognition of species names in text is crucial for biomedical text mining, but current methods, including deep learning, have poor performance. This study introduces the S1000 corpus, which greatly improves the accuracy of species name recognition (F-score = 93.1%) for both deep learning and dictionary-based approaches.