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
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’…