New submission accepted at CoNLL 2019

The following new publication was accepted at the Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL):

[1] BIOfid Dataset: Publishing a German Gold Standard for Named Entity Recognition in Historical Biodiversity Literature

[1]

S. Ahmed, M. Stoeckel, C. Driller, A. Pachzelt, and Alexander Mehler, “BIOfid Dataset: Publishing a German Gold Standard for Named Entity Recognition in Historical Biodiversity Literature,” in Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL), Hong Kong, China, 2019, pp. 871-880.
[Bibtex]

@InProceedings{Ahmed:Stoeckel:Driller:Pachzelt:Mehler:2019,
 author = {Sajawel Ahmed and Manuel Stoeckel and Christine Driller and Adrian Pachzelt and Alexander
Mehler},
 title = {{BIOfid Dataset: Publishing a German Gold Standard for Named Entity Recognition in Historical
Biodiversity Literature}},
 booktitle = {Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)},
 publisher = {Association for Computational Linguistics},
 year = 2019,
    booktitle = "Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)",
    address = "Hong Kong, China",
    url = "https://www.aclweb.org/anthology/K19-1081",
    doi = "10.18653/v1/K19-1081",
    pages = "871--880",
    abstract = "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.",
}