Jyoti Nigania

Hi,i am writing blogs for our platform House of Bots on Artificial Intelligence, Machine Learning, Chatbots, Automation etc after completing my MBA degree. ...

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Hi,i am writing blogs for our platform House of Bots on Artificial Intelligence, Machine Learning, Chatbots, Automation etc after completing my MBA degree.

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Understanding the terms NLP, NLU, NLG and how Chatbot Works?

By Jyoti Nigania |Email | Sep 18, 2018 | 8163 Views

Chatbots can open the doors of endless business opportunities providing the compelling writing and the visuals. They can effectively deal with every aspect of branding while offering a competitive edge to entrepreneurs and leads to higher productivity. Chatbots are talk of the town.
 Two understand what exactly chatbots are and the future of the chatbots. Let's understand the three most common acronyms. 
  • Natural Language Processing (NLP): Natural Language Processing is a blanket term used to describe a machine's ability to ingest what is said to it, break it down, comprehend its meaning, determine appropriate action, and respond back in language the user will understand.

  • Natural Language Understanding (NLU): Natural Language Understanding is a subset of NLP that deals with the much narrower, but equally important facet of how to best handle unstructured inputs and convert them into a structured form that a machine can understand and act upon. While humans are able to effortlessly handle mispronunciations, swapped words, contractions, colloquialisms, and other quirks, machines are less adept at handling unpredictable inputs.

  • Natural Language Generation (NLG): Natural Language Generation simply put is what happens when computers write language. NLG processes turn structured data into text.
Let's take an example that what is the process for communication with another human being is in the following manner when your mother asks you to buy some Tropicana 100% orange juice. Your first question is how much of it does she want? 1Lt or 500ml or 200? She tells you she wants a 1 liter Tropicana 100% Orange Juice. Now you know that regular Tropicana is easily available, but 100% is hard to come by, so you call up a few stores beforehand to see where it's available. You find one store that's pretty close by, so you go back to your mother and tell her you found what she wanted. It's $2, maybe $3, and after asking her for the money, you go on your way. These are the three most important and common acronyms used in designing a chatbots. 

Architecture Diagram for Chatbots:


1. You find a product on Facebook's Messenger and for the sake of consistency, let's say it's the same bottle of Tropicana. You only ever see the presentation layer and send the bot a message that is picked up by the backend saying you want some Tropicana.
2. Using Natural Language Processing (what happens when computers read the language. NLP processes turn text into structured data), the machine converts this plain text request into codified commands for itself.
3. Now the chatbot throws this data into a decision engine since in the bots mind it has certain criteria to meet to exit the conversational loop, notably, the quantity of Tropicana you want.
4. Using Natural Language Generation (what happens when computers write a language. NLG processes turn structured data into text), much like you did with your mother the bot asks you how much of said Tropicana you wanted.
5. This array of responses goes back into the messaging backend and is presented to you in the form of a question. You tell the bot you want 1 liter and we go back through NLP into the decision engine.
6. The bot now analyzes pre-fed data about the product, stores, their locations and their proximity to your location. It identifies the closest store that has this product in stock and tells you what it costs.
7. It then directs you to a payment portal and after it receives confirmation from gateway, it places your order for you, and voila in one to two business days, you have 1 liter of Tropicana 100% Orange Juice.

It is difficult for a machine to better understand the sentiment of the humans but progress is being made in this filed though and soon machines will not only be able to understand what you are saying, but also how you are saying it and what you're feeling while you're saying it. 

Source: HOB