| Glycan Abbreviation | | Glycan-Abbreviation in GlycoNAVI | ISSAKU YAMADA | 2019-07-17 | |
| DPCIRCT | | | Suexuan | 2024-08-20 | |
| DisGeNET5 | | Associations obtained by text mining MEDLINE abstracts using the BeFree system | Yue Wang | 2019-03-11 | |
| CORD-19-sample-annotation | | | Jin-Dong Kim | 2020-04-21 | |
| CORD-19 | | CORD-19 (COVID-19 Open Research Dataset) is a free, open resource for the global research community provided by the Allen Institute for AI: https://pages.semanticscholar.org/coronavirus-research.
As of 2020-03-20, it contains over 29,000 full text articles. This CORD-19 collection at PubAnnotation is prepared for the purpose of collecting annotations to the texts, so that they can be easily accessed and utilized.
If you want to contribute with your annotation,
take the documents in the
CORD-19_All_docs project,
produce your annotation to the texts using your annotation system, and
contribute the annotation back to PubAnnotation (HowTo).
All the contributed annotations will become publicly available.
Please note that, during uploading your annotation data, you do not need to be worried about slight changes in the text: PubAnnotation will automatically catch them and adjust the positions appropriately.
Once you have uploaded your annotation, please notify it to admin@pubannotation.org
admin@pubannotation.org, so that it can be included in this collection, which will make your annotation much easily findable.
Note that as the CORD-19 dataset grows, the documents in this collection also will be updated.
IMPORTANT: CORD-19 License agreement requires that the dataset must be used for text and data mining only. | Jin-Dong Kim | 2020-04-14 | |
| chenxin_473849_800_2 | | | chenxin0102 | 2024-08-27 | |
| chenxin_473849 | | | chenxin0102 | 2024-08-26 | |
| bionlp-st-ge-2016 | | The 2016 edition of the Genia event extraction (GE) task organized within BioNLP-ST 2016 | Jin-Dong Kim | 2019-03-11 | |
| bionlp-ost-19-SeeDev-binary | | SeeDev-binary subtask of the SeeDev task proposed at BioNLP-OST 2019. SeeDev-binary is a binary relation extraction task.
Homepage of SeeDev: https://sites.google.com/view/seedev2019/home | ldeleger | 2020-02-07 | |
| bionlp-ost-19-BB-rel-ner | | BB-rel+ner subtask of the Bacteria Biotope task proposed at BioNLP-OST 2019. BB-rel+ner is an entity recognition and relation extraction task.
Homepage of Bacteria Biotope: https://sites.google.com/view/bb-2019/home | ldeleger | 2020-02-07 | |
| bionlp-ost-19-BB-rel | | BB-rel subtask of the Bacteria Biotope task proposed at BioNLP-OST 2019. BB-rel is a relation extraction task.
Homepage of Bacteria Biotope: https://sites.google.com/view/bb-2019/home
| ldeleger | 2020-02-07 | |
| bionlp-ost-19-BB-norm-ner | | BB-norm+ner subtask of the Bacteria Biotope task proposed at BioNLP-OST 2019. BB-norm+ner is an entity recognition and normalization task. | ldeleger | 2020-02-07 | |
| bionlp-ost-19-BB-norm | | BB-norm subtask of the Bacteria Biotope task proposed at BioNLP-OST 2019.
BB-norm is an entity normalization task. | ldeleger | 2020-02-07 | |
| bionlp-ost-19-BB-kb-ner | | BB-kb+ner subtask of the Bacteria Biotope task proposed at BioNLP-OST 2019. BB-kb+ner is an entity recognition, normalization and relation extraction task.
Homepage of Bacteria Biotope: https://sites.google.com/view/bb-2019/home | ldeleger | 2020-02-07 | |
| bionlp-ost-19-BB-kb | | BB-kb subtask of the Bacteria Biotope task proposed at BioNLP-OST 2019. BB-kb is an entity normalization and relation extraction task.
Homepage of Bacteria Biotope: https://sites.google.com/view/bb-2019/home | ldeleger | 2020-02-07 | |
| Annotation of Human Phenotype-Gene Relations - Identification of Negative, False, and Unknown Relations | | Accessible negative results are relevant for researchers and clinicians not only to limit their search space but also to prevent the costly re-exploration of the hypothesis. However, most biomedical relation extraction data sets do not seek to distinguish between a false and a negative relation. A false relation should express a context where the entities are not related. In contrast, a negative relation should express a context where there is an affirmation of no association between the two entities. Furthermore, when we are dealing with data sets created using distant supervision techniques, we also have some false negative relations that constitute undocumented/unknown relations. Unknown relations are good examples to further exploration by researchers and clinicians. We propose to improve the distinction between these two concepts, by revising the false relations of the PGR corpus with regular expressions. | dpavot | 2020-02-21 | |
| AnEM | | the largest manually annotated corpus on anatomical entities | Yue Wang | 2019-04-03 | |
| AGAC | | This is the collection for AGAC track data, a subtask in BioNLP OST 2019, Hong Kong. | xiajingbo | 2019-03-13 | |
| 190926 | | | nakazato | 2019-09-26 | |
