Training Spacy's Named Entity Recognition to Recognize Drugs – NLP in Python




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In this tutorial we will learn how to create a dataset and train Spacy’s Named Entity Recognition to identify Drugs as a new entity using the Drug Reviews Dataset.

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Official Docs:https://spacy.io/usage/training
Github:https://github.com/Jcharis/data-science-projects
Credits :@curiousprogrammer

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Comment List

  • Lead Learner
    January 22, 2021

    Cant find the notebook in the github link!

  • Lead Learner
    January 22, 2021

    Is the TEXT body important for TEXT , annotation in TRAIN_DATA ? 

    Its a lot simpler to just train the NER directly off the name of the drug rather than having to manually/automatically create an entire sentence around the drug name..say for example:

    medList = ['ibuprofen' , ''paracetemol' , 'panadol' ]
    TRAIN_DATA = [(med_name , { 'entities' : [ (0 , len(med_name)-1 , 'DRUG' ) ] } ) for med_name in medList ]

    You reckon any issues/downside with training the NER with just the drug name only?

    Thanks for the tutorials nonetheless!

  • Lead Learner
    January 22, 2021

    excellent tutorial, why we are doing count < 1000 here?

  • Lead Learner
    January 22, 2021

    Thank you for this amazing tutorial. I have a question though, What if there is no column such as drug_name in the dataset?. How do we train the model in that case?

  • Lead Learner
    January 22, 2021

    Thank you very much it is a great tutorial, can please upload tutorial on pyTorch for text

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