

# 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}
}

```

multilinguality:monolingual language:en license:cc0-1.0 region:us
{"language": ["en"], "license": "cc0-1.0", "multilinguality": "monolingual", "pretty_name": "NLM-Chem", "bigbio_language": ["English"], "bigbio_license_shortname": "CC0_1p0", "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"]}
TAGS
#multilinguality-monolingual #language-English #license-cc0-1.0 #region-us 



# Dataset Card for NLM-Chem

## Dataset Description

- Homepage: URL
- 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.







# Dataset Card for NLM-Chem ## Dataset Description

- Homepage: URL
- 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.
TAGS
#multilinguality-monolingual #language-English #license-cc0-1.0 #region-us 
 # Dataset Card for NLM-Chem ## Dataset Description

- Homepage: URL
- 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.
passage: TAGS
#multilinguality-monolingual #language-English #license-cc0-1.0 #region-us 
# Dataset Card for NLM-Chem## Dataset Description

- Homepage: URL
- 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.