Implement CoNECo dataset (#931)

* Implement CoNECo dataset

* fix style

* fix issues raised in comments

* fix licence

* fix citetion in README

* fix flake8

* filter out of scope entities

---------

Co-authored-by: Oğuz Şerbetçi <[email protected]>

bigbio/hub/hub_repos/coneco/README.md

@@ -0,0 +1,57 @@
+---
+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},
+}
+```