Named entity recognition (NER) consists of extracting 'entities' from text - what we mean by that is given the sentence:
"Apple reached an all-time high stock price of 143 dollars this January."
We might want to extract the key pieces of information - or 'entities' - and categorize each of those entities. Like so:
- Apple : Organization
- 143 dollars : Monetary Value
- this January : Date
For us humans, this is easy. But how can we teach a machine to distinguish between a granny smith apple and the Apple we trade on NASDAQ?
(No, we can't rely on the 'A' being capitalized…)
This is where NER comes in - using NER, we can extract keywords like apple and identify that it is, in fact, an organization - not a fruit.
The go-to library for NER is spaCy, which is incredible. But what if we added transformers to spaCy? Even better - we'll cover exactly that in this video.
🤖 70% Discount on the NLP With Transformers in Python course:
https://bit.ly/3DFvvY5
