S1000: a better taxonomic name corpus for biomedical information extraction




Luoma Jouni, Nastou Katerina, Ohta Tomoko, Toivonen Harttu, Pafilis Evangelos, Jensen Lars Juhl, Pyysalo Sampo

PublisherOXFORD UNIV PRESS

2023

Bioinformatics

BIOINFORMATICS

BIOINFORMATICS

btad369

39

6

8

1367-4803

1367-4811

DOIhttps://doi.org/10.1093/bioinformatics/btad369(external)

https://doi.org/10.1093/bioinformatics/btad369(external)

https://research.utu.fi/converis/portal/detail/Publication/180376416(external)



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 implementation

All resources introduced in this study are available under open licenses from https://jensenlab.org/resources/s1000/. The webpage contains links to a Zenodo project and three GitHub repositories associated with the study.


Last updated on 2024-26-11 at 20:29