|
|
--- |
|
|
language: |
|
|
- en |
|
|
bigbio_language: |
|
|
- English |
|
|
license: cc-by-4.0 |
|
|
bigbio_license_shortname: CC_BY_4p0 |
|
|
multilinguality: monolingual |
|
|
pretty_name: CoNECo |
|
|
homepage: https://zenodo.org/records/11263147 |
|
|
bigbio_pubmed: false |
|
|
bigbio_public: true |
|
|
bigbio_tasks: |
|
|
- NAMED_ENTITY_RECOGNITION |
|
|
- NAMED_ENTITY_DISAMBIGUATION |
|
|
paperswithcode_id: coneco |
|
|
--- |
|
|
|
|
|
|
|
|
# Dataset Card for CoNECo |
|
|
|
|
|
## Dataset Description |
|
|
|
|
|
- **Homepage:** https://zenodo.org/records/11263147 |
|
|
- **Pubmed:** False |
|
|
- **Public:** True |
|
|
- **Tasks:** NER, NEN |
|
|
|
|
|
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}, |
|
|
} |
|
|
``` |
|
|
|
