metadata

language:
  - en
bigbio_language:
  - English
license: cc0-1.0
multilinguality: monolingual
bigbio_license_shortname: CC0_1p0
pretty_name: NLM-Chem
homepage: https://biocreative.bioinformatics.udel.edu/tasks/biocreative-vii/track-2
bigbio_pubmed: true
bigbio_public: true
bigbio_tasks:
  - NAMED_ENTITY_RECOGNITION
  - NAMED_ENTITY_DISAMBIGUATION
  - TEXT_CLASSIFICATION

Dataset Card for NLM-Chem

Dataset Description

Homepage: https://biocreative.bioinformatics.udel.edu/tasks/biocreative-vii/track-2
Pubmed: True
Public: True
Tasks: NER,NED,TXTCLASS

NLM-Chem corpus consists of 150 full-text articles from the PubMed Central Open Access dataset, comprising 67 different chemical journals, aiming to cover a general distribution of usage of chemical names in the biomedical literature. Articles were selected so that human annotation was most valuable (meaning that they were rich in bio-entities, and current state-of-the-art named entity recognition systems disagreed on bio-entity recognition.

Citation Information

@Article{islamaj2021nlm,
title={NLM-Chem, a new resource for chemical entity recognition in PubMed full text literature},
author={Islamaj, Rezarta and Leaman, Robert and Kim, Sun and Kwon, Dongseop and Wei, Chih-Hsuan and Comeau, Donald C and Peng, Yifan and Cissel, David and Coss, Cathleen and Fisher, Carol and others},
journal={Scientific Data},
volume={8},
number={1},
pages={1--12},
year={2021},
publisher={Nature Publishing Group}
}