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A Knowledge Graph resource of NLP-progress #617
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I am sure, my team at NFDI4DS (https://www.nfdi4datascience.de/) would also be happy to help to convert data |
This is a great set of resources! What do you think would be the best way to integrate them? |
@sebastianruder could we perhaps schedule a call as a starting point, where I could present the ORKG and its features to you? Perhaps then we could elicit a set of requirements to integrate the data. My contact information is here https://sites.google.com/view/jen-web/contact?authuser=0 @RicardoUsbeck happy to hear your thoughts on how best you think perhaps we could go about it, also relaying the information to your team at NFDI4DS. :) |
Sure, will do. Actual work will start in Oct. We wanted to crawl this website to feed the (O)RKG. On the other hand, it would be nice to have a manual (?) export form ORKG (which will undoubtedly grow faster) here...but there are also downsides to this approach. Happy to help you discuss ideas. |
Indeed I fully agree with having crawling scripts for the website to structure the data. Perhaps then additional curational support on top of it, to ensure the data quality... If the resulting dataset can be structured in an excel sheet and is only a one level graph, the Furthermore, I kindly suggest also that a template can be defined https://orkg.org/templates for such data enabling new users to seamlessly leverage the defined template when adding new data. Happy to continue the discussion thread. Please let me know. |
Hi both, are you ok meeting without me? I think you are both more up-to-date on this type of data. I'm happy to go with whatever you decide, as long as it can be reasonably integrated into the website. |
@sebastianruder Will be happy to share updates here, in due course. |
Dear authors, this repository is such a great resource! Many thanks for creating it. I would like to suggest that maybe the Open Research Knowledge Graph (https://orkg.org/) could be leveraged to enlist such resources for persistence, knowledge sharing, and querying. Please find below some resources I created related to the information in this repository.
Named Entity Recognition Tasks in the MUC series
https://orkg.org/comparison/R162797/
NER in the Automatic Content Extraction (ACE) Series
https://orkg.org/comparison/R162851/
Named Entity Recognition in the CoNLL Series and the OntoNotes corpus as a related resource
https://orkg.org/comparison/R166315/
Named Entity Recognition Based on Wikipedia
https://orkg.org/comparison/R166240/
A comparison of the annotated resources of software mentions in scholarly articles
https://orkg.org/comparison/R166560/
NLP Datasets for Named Entity Recognition and Relation Extraction from Biomedicine Scholarly Articles
https://orkg.org/comparison/R163265/
Comparisons and Visualizations of the CrossNER Benchmark Corpus for its Source and Target Domains
https://orkg.org/comparison/R163843/
Surveying BioNLP Shared Tasks Corpora for Named Entity Recognition
https://orkg.org/comparison/R165702/
Surveying BioCreAtIvE Shared Tasks Corpora for Named Entity Recognition
https://orkg.org/comparison/R172155/
The benefits of such machine-encoded data is that Reviews can be automatically created thereby.
Surveying the BioCreAtIvE Shared Task Series
https://orkg.org/review/R172166
Surveying the BioNLP Shared Task Series
https://orkg.org/review/R165924
I would be very happy to offer support in this direction. :)
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