His stomach growls with hunger pangs and hastily he grunts out the words, "Large pep pizza and a liter of soda." Instantly the home virtual assistant comes to life and repeats back in an eerily human voice. "Bob, you want a large pepperoni pizza and a liter of soda correct?" "Yeah," grunts the man as he dials in a new show on his remote and pours the remaining crumbs of the potato chip bag into his mouth. Quickly the AI assistant calls the local pizza parlor and a friendly "employee" answers. "Speedy Pizza, how may I help you?" "Hi, I'd like to place an order for a large pepperoni pizza and a liter of soda for delivery," requests Bob's AI assistant in a cheerful and pleasant demeanor.

In a previous post called "How to make your chatbot more human-like," I expanded on some of the most common issues users face while talking to a bot, and I explained how developers can solve them by using NLP. After conducting research and trying all the major bot development platforms, I realized the need for a long and intensive training to provide accurate answers to users' requests. Chatbot training is a resource intensive task. Linguistic analysis provides different solutions that speed up training and, most importantly, solve some structural issues with bot development. Most chatbot frameworks are based around the concept of intent and entity detection, which involves identifying both the intent of an utterance and the entities relevant to that intent.