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BiodivNERE: Gold standard corpora for named entity recognition and relation extraction in the biodiversity domain.

Nora AbdelmageedFelicitas LöfflerLeila FeddoulAlsayed AlgergawySheeba SamuelJitendra GaikwadAnahita KazemBirgitta König-Ries
Published in: Biodiversity data journal (2022)
In this paper, we present two gold-standard corpora for Named Entity Recognition (NER) and Relation Extraction (RE) generated from biodiversity datasets metadata and abstracts that can be used as evaluation benchmarks for the development of new computer-supported tools that require machine learning or deep learning techniques. These corpora are manually labelled and verified by biodiversity experts. In addition, we explain the detailed steps of constructing these datasets. Moreover, we demonstrate the underlying ontology for the classes and relations used to annotate such corpora.
Keyphrases
  • deep learning
  • machine learning
  • artificial intelligence
  • rna seq
  • convolutional neural network
  • big data