By Neha M
Hello! Today I will be talking about one of the most hyped topics of AI and my personal favourite Natural Language Processing, in short, NLP. Before going through what all this hype is about, let’s see how the concept originated in the first place.
In 1950, Alan Turing proposed the well-known ‘Turing Test’ in his paper, ‘Computing Machinery and Intelligence’, where he introduces a modified version of an ‘Imitation Game’. In this test, human judge ‘C’ should determine who among A (a machine) and B (a human) is a human, over interrogations. Any machine which would be able to fool the interrogator, would pass the test. Turing argues that, any machine with sufficient physical resources, could be programmed to give answers as close a human can give. This is believed to be the foundation of NLP that we see today. Soon after this, several attempts were made in the field of NLP – structural transformation of natural language into machine readable format, complex rule based systems were created to make computers understand the natural language. Since these were not satisfactorily good enough, NLP and AI seemed to lose its charm. By the 1980s, with the scope of machine learning increasing, NLP started shifting towards machine learning too. In 1985, Terry Sejnowski created a neural system which could learn how to pronounce English words. Today, a tremendous amount of work is being done in the field of NLP using Deep Neural Networks or Machine Learning in general, where we are able to create state-of-the-art models in text classification, QnA generation, Sentiment Classification, etc.
In simple words, Natural Language Processing involves machines learning to understand language that humans speak, analyse it, manipulate it and give intended results.
Well, ‘Processing’ is a broad term. In order to understand the role of NLP, we will have to look into some scenarios.
Firstly, the most obvious point – Natural Language is the language that we speak. So if a machine can understand the language we speak, it makes the interaction between a machine and a human, much smoother.
Imagine the amount of textual and speech data we have over the internet today – A lot!!! A lot of information is always good, but it is obvious that allocating human resources for processing such an enormous amount of data isn’t really practical. NLP has certainly automated the process of analysing and extracting relevant information out of large volumes of data.
Suppose, the market strategist of a company wants to know how people have reacted to their new promotional event. People will have expressed their views over any social media or other internet platforms out there. What does the task include in general –
When you observe the above steps, each of the above steps needs NLP in one or the other way since we are ultimately dealing with the text.
Since NLP itself is a vast field, the way concepts of NLP are being used in various domains is enormous. Here I have listed out some of the most important applications of NLP, which are being used in a variety of projects today!
Today we have Google Translate, Bing Translator, etc providing us with free translation services. Many applications allow us to translate conversations in the app, for example – Facebook.
I hope, after reading this blog, you have quite an idea of how versatile NLP is! Do follow our blog page for more such insights on AI and Conversational AI Chatbots.
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