|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-PT), providing this resource to the community to improve the text mining research on COVID-19-related literature.||dpavot||2021-02-17|
|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.||pruas_18||2021-01-20|
|LitCovid-sample||Various annotations to a sample set of LitCovid, to demonstrate potential of harmonized various annotations.||Jin-Dong Kim||2021-01-14|
|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:
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,
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 firstname.lastname@example.org.
|med-device-indications||PMA approval statements describing indications of class III devices||therightstef||2020-02-05|
|PIR||Protein Information Resource (PIR)||Yue Wang||2019-03-12|
|PMID_PMC||Updated annotation(s) of PMC data and results||alo33||2023-03-20|
|Preeclampsia||Preeclampsia-related annotations for text mining||Jin-Dong Kim||2019-03-10|
|WMT Biomedical Task||wmtbio||2020-12-18|