Custom NLP: Chapter 1

NLP May 22, 2021

In our previous series we explored the possibilities of NLP and the platforms that can be leveraged to make it happen.

  1. Natural Language Processing: Chapter 1
  2. NLP Chapter 2: Google Cloud Platform
  3. NLP Chapter 3: Spacy

In today's article we are going to explore the world of Custom NLP.

What does it mean?

Natural Language Processing encompasses Analysing, Comprehending, Interpreting and Summarising Human Linguistics.

But many a times it happens that learnings received from another individual may not prove as effective for you as it did for the individual.

After all Experience Matters.

In metaphorical language Experience = Data and Effective = Accuracy. As every human has different needs and problems to deal with, in the same manner every AI is built for a niche problem or a specific use case.

When the system is designed with the Idea of resolving a Generic use case, it only makes sense to expose it to a vast variety and expanse of DATASET. But if one's needs were personalised or were to cater to a smaller domain, it would only make sense to design your system accordingly.

As Jack of all trades, Master of none.

Doesn't work well for Tailor Made Solutions

In simple words, for solutions aimed at resolving for a target domain or a niche set of people, it requires Custom built Architectures and Data to attain benchmarking results and accuracy. These Custom Built Architectures catering to Human Linguistics is called Custom NLP.

How can we do it?

As part of our previous posts we have seen the various use cases of NLP. Now the question is WHAT do we need? and HOW can we make it happen?

Now that you are aware of the steps involved in building a Custom NLP solution. The next obvious question is Where do we begin?

Where do we begin?

There are many tools and frameworks that can help you with the process to achieve your goals. The choice is yours whether you want to pick Cloud Native solutions or Open Source tools to get to the Finish Line.

Just on a side note some of the platforms that you can explore -

  1. AutoML - Google Cloud Platform
  2. Label Studio - Data Annotation
  3. SageMaker Auto Pilot- Amazon Web Services

As a part of this series we will be taking you step by step through the complete journey from Data Gathering to Annotating to Training to Deploying. We will be deep diving into every concept and not just the theory but also walking you through on HOW to implement it.

STAY TUNED for more content on Custom NLP in our future posts. 😁

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Vaibhav Satpathy

AI Enthusiast and Explorer

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