> ホーム

ドキュメント

PubMed	12,920,929
PMC	134,094
CORD-19	36,823
PubMed-fre	9,378
PubMed-spa	8,694
PubMed-ger	6,732
PubMed-rus	6,029
PubMed-por	4,185
PubMed-chi	3,697
PubMed-ita	1,674
FirstAuthors	1,276
PubMed-jpn	223
GrayAnatomy	66

プロジェクト (468)

	手動	自動	Sum
公開中	45	25	70
ベータ	8	16	24
開発中	105	57	162
テスト中	97	49	148
N/S	48	5	53
Sum	311	155	468

グループ (33)

名前	説明	proj.数	最終更新日
LASIGE(old)	The global motivation is the creation of parallel multilingual datasets for text mining systems in COVID-19-related literature. The expected contribution of the project will be the annotation of a multilingual parallel dataset (EN-ES and EN-PT), providing this resource to the community to improve the text mining research on COVID-19-related literature.	2	2021-01-20
LitCovid		14	2021-01-18
LitCovid-sample	Various annotations to a sample set of LitCovid, to demonstrate potential of harmonized various annotations.	16	2021-01-14
WMT Biomedical Task		0	2020-12-18
LitCovid-v1	This collection includes the result from the Covid-19 Virtual Hackathon. LitCovid is a comprehensive literature resource on the subject of Covid-19 collected by NCBI: https://www.ncbi.nlm.nih.gov/research/coronavirus/ Since the literature dataset was released, several groups are producing annotations to the dataset. To facilitate a venue for aggregating the valuable resources which are highly relevant to each other, and should be much more useful when they can be accessed together, this PubAnnotation collection is set up. It is a part of the Covid19-PubAnnotation project. In this collection, the LitCovid-docs project contains all the documents contained in the LitCovid literature collection, and the other projects are annotation datasets contributed by various groups. It is an open collection, which means anyone who wants to contribute can do so, in the following way: take the documents in the, LitCovid-docs project produce annotation to the texts based on your resource, and contribute the annotation back to this collection: create your own project at PubAnnotaiton, upload your annotation to the project (HowTo), and add the project to this collection. 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. Should you have any question, please feel free to mail to admin@pubannotation.org.	12	2020-11-20
new_collection		1	2020-09-29
Testing		0	2020-05-31
CORD-19-sample-annotation		10	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.	12	2020-04-14
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.	3	2020-02-21

Shared Tasks (12)

名前	説明	最終更新日
LASIGE: Annotating a multilingual COVID-19-related corpus for BLAH7	The global motivation is the creation of parallel multilingual datasets for text mining systems in COVID-19-related literature. Tracking the most recent advances in the COVID-19-related research is essential given the novelty of the disease and its impact on society. Still, the pace of publication requires automatic approaches to access and organize the knowledge that keeps being produced every day. It is necessary to develop text mining pipelines to assist in that task, which is only possible with evaluation datasets. However, there is a lack of COVID-19-related datasets, even more, if considering other languages besides English. The expected contribution of the project will be the annotation of a multilingual parallel dataset (EN-ES and EN-PT), providing this resource to the community to improve the text mining research on COVID-19-related literature.	2021-01-20
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	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	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	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	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	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.	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.	2020-02-07
190926		2019-09-26
RDoCTask		2019-03-16