Who doesn't feel comfortable in their own skin?
The truth bespoken everyone likes to interact and converse in a language that they are well versed with. For animals it's phonetics, whereas for us humans it's a slightly evolved version of the same. Over the centuries, the human language has seen phenomenal growth and maturity in terms of their scripts, literature or vocal mode of communication.
What is interesting to note is that, no other species Alive or Extinct is believed to have such highly evolved mode of communication.
Well that statement is not completely true. If I may add, in the past decade the advancements in the field of Natural Language Processing (NLP) has practically made Humans able to interact with Machines in their native languages. That would potentially imply that there is one entity that can comprehend human language as good as humanity in itself and the funny part is it is not Alive. But then again that is a whole another story to debate about.
NLP is that magic wand which gives machines the super powers to Understand, Read, Comprehend and Interpret human language. It has various sub branches which are noteworthy of highlighting -
Natural Language Understanding (NLU)
Natural Language Generation (NLG)
Natural Language Query (NLQ)
The Global Natural Language Processing (NLP) Market Size is expected to grow from 11.6 Billion USD in 2020 to 35.1 Billion USD by 2026, at a Compounded Annual Growth Rate (CAGR) of 20.3% during the Forecast Period.
What is NLP ?
To be simply put, if a machine can comprehend and execute required tasks from human language and interpret the context of the conversation, the machine is said to be Intelligent and the technology used to process it is called Natural Language Processing.
Let's take some examples to understand the functionality better. For arguments sake, say two people are having a conversation. Every time an individual tries to communicate, he/she can do that via various means. They can pen down their thoughts or speak it out loud. This where the game begins.
- Our human mind converts our cognitive feed, be it visual or phonic into a combination of alpha numeric statements in their sequential order of occurrence.
- It then tries to gather environmental information from the statements, which we also call as entities or Natural Language Entity Recognition (NER).
- Once we have the highlights of the conversation, we move towards identifying What needs to be done and How it has to happen?
- The above mentioned points is a Basic AI with NLP capabilities. The further stages is where we get closer to mimicking human thought process.
- Now that we know what has to be done and how, the next obvious question that our mind struggles with is Why should I do it?
- Once we have gathered all the necessary insights, our brain moves towards understanding the implications of that information and how would it effect the individual and the surroundings.
Building Blocks of NLP
What we mentioned above are the basic steps involved in the process of NLP. Now if we were to draw parallels on how it would reflect from a technical standpoint, the system would have to perform some of the below mentioned tasks -
The complete domain list under the umbrella of NLP just is innumerable. Still if we were to consider from the little understanding that we hold, each building block is responsible for Transforming the Data into a more comprehensible format and every bit of that information proves out to be handy in resolving variety of problem statements that one might encounter or try to resolve in the real world.
One of the most significant domain of research involved with NLP revolves around Linguistics. Most of the existing Speech to Text services offered by the top cloud platforms have heavy duty Language Models running exhaustive computations on their backend to provide us with the best possible results. Language models play a very crucial role is reducing the error rate of the engine over time. It caters to not only Textual information but also Vocal (phonic/sound) data as perceived by the system.
Where to Implement NLP?
One of the biggest challenges of any technology related with AI is identifying and understanding the scope of implementation of the system. Some ideas that we develop are either too futuristic for the world to accept, on the other hand sometimes they are too primitive to be scaled up into the real world.
Having the eye and knack to be able to find the needle in the hay stack is very important while dealing with such evolved technologies.
In order to help you through this process and develop the taste for same, let's take a look at some of the ways in which NLP can be applied to our world.
Be it now or centuries into the future, Language is a basic means of communication that will never fade away from our civilisation and if having a system to simplify our mundane tasks using the same linguistics is not a dream, then I believe its the world we need to build.
I hope this article helps you understand the fundamentals involved with Natural Language Processing and helps you build solutions with more ease.
For more content related to NLP over cloud or edge systems, STAY TUNED and keep an eye out for our next release. 😁