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---
language:
- en
bigbio_language:
- English
license: unknown
multilinguality: monolingual
bigbio_license_shortname: UNKNOWN
pretty_name: CHEMDNER
homepage: https://biocreative.bioinformatics.udel.edu/resources/biocreative-iv/chemdner-corpus/
bigbio_pubmed: True
bigbio_public: True
bigbio_tasks:
- NAMED_ENTITY_RECOGNITION
- TEXT_CLASSIFICATION
---
# Dataset Card for CHEMDNER
## Dataset Description
- **Homepage:** https://biocreative.bioinformatics.udel.edu/resources/biocreative-iv/chemdner-corpus/
- **Pubmed:** True
- **Public:** True
- **Tasks:** NER,TXTCLASS
We present the CHEMDNER corpus, a collection of 10,000 PubMed abstracts that
contain a total of 84,355 chemical entity mentions labeled manually by expert
chemistry literature curators, following annotation guidelines specifically
defined for this task. The abstracts of the CHEMDNER corpus were selected to be
representative for all major chemical disciplines. Each of the chemical entity
mentions was manually labeled according to its structure-associated chemical
entity mention (SACEM) class: abbreviation, family, formula, identifier,
multiple, systematic and trivial.
## Citation Information
```
@article{Krallinger2015,
title = {The CHEMDNER corpus of chemicals and drugs and its annotation principles},
author = {
Krallinger, Martin and Rabal, Obdulia and Leitner, Florian and Vazquez,
Miguel and Salgado, David and Lu, Zhiyong and Leaman, Robert and Lu, Yanan
and Ji, Donghong and Lowe, Daniel M. and Sayle, Roger A. and
Batista-Navarro, Riza Theresa and Rak, Rafal and Huber, Torsten and
Rockt{"a}schel, Tim and Matos, S{'e}rgio and Campos, David and Tang,
Buzhou and Xu, Hua and Munkhdalai, Tsendsuren and Ryu, Keun Ho and Ramanan,
S. V. and Nathan, Senthil and { {Z}}itnik, Slavko and Bajec, Marko and
Weber, Lutz and Irmer, Matthias and Akhondi, Saber A. and Kors, Jan A. and
Xu, Shuo and An, Xin and Sikdar, Utpal Kumar and Ekbal, Asif and Yoshioka,
Masaharu and Dieb, Thaer M. and Choi, Miji and Verspoor, Karin and Khabsa,
Madian and Giles, C. Lee and Liu, Hongfang and Ravikumar, Komandur
Elayavilli and Lamurias, Andre and Couto, Francisco M. and Dai, Hong-Jie
and Tsai, Richard Tzong-Han and Ata, Caglar and Can, Tolga and Usi{'e},
Anabel and Alves, Rui and Segura-Bedmar, Isabel and Mart{'i}nez, Paloma
and Oyarzabal, Julen and Valencia, Alfonso
},
year = 2015,
month = {Jan},
day = 19,
journal = {Journal of Cheminformatics},
volume = 7,
number = 1,
pages = {S2},
doi = {10.1186/1758-2946-7-S1-S2},
issn = {1758-2946},
url = {https://doi.org/10.1186/1758-2946-7-S1-S2},
abstract = {
The automatic extraction of chemical information from text requires the
recognition of chemical entity mentions as one of its key steps. When
developing supervised named entity recognition (NER) systems, the
availability of a large, manually annotated text corpus is desirable.
Furthermore, large corpora permit the robust evaluation and comparison of
different approaches that detect chemicals in documents. We present the
CHEMDNER corpus, a collection of 10,000 PubMed abstracts that contain a
total of 84,355 chemical entity mentions labeled manually by expert
chemistry literature curators, following annotation guidelines specifically
defined for this task. The abstracts of the CHEMDNER corpus were selected
to be representative for all major chemical disciplines. Each of the
chemical entity mentions was manually labeled according to its
structure-associated chemical entity mention (SACEM) class: abbreviation,
family, formula, identifier, multiple, systematic and trivial. The
difficulty and consistency of tagging chemicals in text was measured using
an agreement study between annotators, obtaining a percentage agreement of
91. For a subset of the CHEMDNER corpus (the test set of 3,000 abstracts)
we provide not only the Gold Standard manual annotations, but also mentions
automatically detected by the 26 teams that participated in the BioCreative
IV CHEMDNER chemical mention recognition task. In addition, we release the
CHEMDNER silver standard corpus of automatically extracted mentions from
17,000 randomly selected PubMed abstracts. A version of the CHEMDNER corpus
in the BioC format has been generated as well. We propose a standard for
required minimum information about entity annotations for the construction
of domain specific corpora on chemical and drug entities. The CHEMDNER
corpus and annotation guidelines are available at:
ttp://www.biocreative.org/resources/biocreative-iv/chemdner-corpus/
}
}
```
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