Named Entity Recognition with Deep Learning (BERT) — The Essential Guide

From data preparation to model training for NER tasks — and how to tag your own sentences

Nikos Kafritsas
Towards Data Science

Photo by Aaron Burden on Unsplash

Update: You can now learn how to deploy this model with Streamlit!

Nowadays, NLP has become synonymous with Deep Learning.

But, Deep Learning is not the ‘magic bullet’ for every NLP task. For example, in sentence classification tasks, a simple linear classifier could work reasonably well. Especially if you have a small training dataset.

However, some NLP tasks flourish with Deep Learning. One such task is Named Entity Recognition — NER:

NER is the process of identifying and classifying named entities into predefined entity categories.

For instance, in the sentence:

Nick lives in Greece and works a Data Scientist.

We have 2 entities:

  • Nick, which is a ‘Person’.
  • Greece, which is a ‘Location’.

Therefore, given the above sentence, a classifier should be able to locate the two terms (‘Nick’, ‘Greece’) and correctly classify them as ‘Person’ and ‘Location’…

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Published in Towards Data Science

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Written by Nikos Kafritsas

Data Scientist @ Persado || 🥇Top Writer in Artificial Intelligence and Time Series

Responses (5)

What are your thoughts?

Very insightful
Finally some technical content!
Thanks

Interesting 😊 thanks for sharing

Technical but screenshots of syntax helps a bit