6533b7d5fe1ef96bd1263d77
RESEARCH PRODUCT
BIOfid dataset: publishing a German gold standard for named entity recognition in historical biodiversity literature
Christine DrillerManuel StoeckelSajawel AhmedAlexander MehlerAdrian Pachzeltsubject
Biological dataService (systems architecture)Information retrievalbusiness.industryComputer science02 engineering and technologyScientific literature010501 environmental sciencescomputer.software_genre01 natural scienceslanguage.human_languageField (computer science)GermanInformation extractionNamed-entity recognitionPublishingddc:020ddc:5700202 electrical engineering electronic engineering information engineeringlanguage020201 artificial intelligence & image processingArtificial intelligencebusinesscomputer0105 earth and related environmental sciencesdescription
The Specialized Information Service Biodiversity Research (BIOfid) has been launched to mobilize valuable biological data from printed literature hidden in German libraries for over the past 250 years. In this project, we annotate German texts converted by OCR from historical scientific literature on the biodiversity of plants, birds, moths and butterflies. Our work enables the automatic extraction of biological information previously buried in the mass of papers and volumes. For this purpose, we generated training data for the tasks of Named Entity Recognition (NER) and Taxa Recognition (TR) in biological documents. We use this data to train a number of leading machine learning tools and create a gold standard for TR in biodiversity literature. More specifically, we perform a practical analysis of our newly generated BIOfid dataset through various downstream-task evaluations and establish a new state of the art for TR with 80.23% F-score. In this sense, our paper lays the foundations for future work in the field of information extraction in biology texts.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2019-01-01 |