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NLP / Named Entity Recognition + Linking #67
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Here is a publication with some interesting ressources for our NLP tasks: https://www.nature.com/articles/s41597-020-0543-2 |
I added an extract from the introdcution to the Nature article to #35, the researcher use case as it included a good description of what researchers might be looking for from a system like CovidGraph. |
Hello there everyone! I came here because @yGuy made me aware of the project. BioBERT has been used very successfully on the COVID-19 papers (see https://covidask.korea.ac.kr/). However I think this is still an interesting issue to tackle, specifically it could be tested if the newest advances in RNN architectures (GPT3 by OpenAI) can achieve even better performance. I talked with my supervisor @coltekin (University of Tuebingen) and it seems like a suitable collaboration for my bachelors thesis in computational linguistics. If this sounds interesting, it would be good to have a discrete list of entities that are really relevant to investigate. Lennard |
Hi Lennard,
YES! 🚀 i was allready peering at gpt2/3 :) Also we are trying to get our hand on some more hardware (GPUs) to speed up computing if applicable in a later stage. |
We need additional expertise to assist with a number of issues that could benefit from NLP/Entity recognition. Our goal is to identify and create more meaningful relationships in currently unconnected sub-graphs within CovidGraph.
Our ethos is open and transparent so we would prefer open source solutions.
For example, within publication & patent text to identify:
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