Datasets:

Languages:
English
License:
Dataset Viewer

The viewer is disabled because this dataset repo requires arbitrary Python code execution. Please consider removing the loading script and relying on automated data support (you can use convert_to_parquet from the datasets library). If this is not possible, please open a discussion for direct help.

Dataset Card for CoNECo

Complex Named Entity Corpus (CoNECo) is an annotated corpus for NER and NEN of protein-containing complexes. CoNECo comprises 1,621 documents with 2,052 entities, 1,976 of which are normalized to Gene Ontology. We divided the corpus into training, development, and test sets.

Citation Information

@article{10.1093/bioadv/vbae116,
    author = {Nastou, Katerina and Koutrouli, Mikaela and Pyysalo, Sampo and Jensen, Lars Juhl},
    title = "{CoNECo: A Corpus for Named Entity Recognition and Normalization of Protein Complexes}",
    journal = {Bioinformatics Advances},
    pages = {vbae116},
    year = {2024},
    month = {08},
    abstract = "{Despite significant progress in biomedical information extraction, there is a lack of resources \
for Named Entity Recognition (NER) and Normalization (NEN) of protein-containing complexes. Current resources \
inadequately address the recognition of protein-containing complex names across different organisms, underscoring \
the crucial need for a dedicated corpus.We introduce the Complex Named Entity Corpus (CoNECo), an annotated \
corpus for NER and NEN of complexes. CoNECo comprises 1,621 documents with 2,052 entities, 1,976 of which are \
normalized to Gene Ontology. We divided the corpus into training, development, and test sets and trained both a \
transformer-based and dictionary-based tagger on them. Evaluation on the test set demonstrated robust performance, \
with F-scores of 73.7\\% and 61.2\\%, respectively. Subsequently, we applied the best taggers for comprehensive \
tagging of the entire openly accessible biomedical literature.All resources, including the annotated corpus, \
training data, and code, are available to the community through Zenodo https://zenodo.org/records/11263147 and \
GitHub https://zenodo.org/records/10693653.}",
    issn = {2635-0041},
    doi = {10.1093/bioadv/vbae116},
    url = {https://doi.org/10.1093/bioadv/vbae116},
    eprint = {https://academic.oup.com/bioinformaticsadvances/advance-article-pdf/doi/10.1093/bioadv/vbae116/58869902/vbae116.pdf},
}
Downloads last month
49