S
Sebastian Witalec
Guest
Natural Language Processing is what allows chatbots to analyse input and respond appropriately. In this article you'll get a general idea of what NLP is and how it works.
Natural Language Processing is the ability of a computer program to understand human language as it is spoken.
In other words, NLP is the mechanism that allows chatbots—like NativeChat—to analyse what users say, extract essential information and respond with appropriate answers.
For example, if the user says: "When does your shop open?", the chatbot should be able to match this question to an opening-hours conversation, and respond with: "The shop opens from 9 AM to 5 PM".
What You are Going to Learn
The purpose of this article is to give you a general idea of what NLP is and how it works.
The idea is that with a better understanding of the NLP you should be able to train your chatbots to be able to understand their users a lot better.
There are many ways of handling each stage of the NLP processing, however, we will not go into too many details, as the aim of this article is to focus on the bigger picture.
The High-Level Understanding
From a high-level view, Natural Language Processing works in two stages:
NLP Model Training
In this stage, we (as the people that train our chatbots) provide our chatbots with a list of expressions that we would like them to associate with various conversations.
NLP takes all the training expressions and creates a model, which can be used to understand what the users say.
Each time a user says something, NLP takes the user input and matches to the NLP Model, analyses the results and responds with an answer or follow-up question.
NLP Model Training
Before we let our chatbots interact with the users, we need to train the NLP Model. But what does that even mean?
This is like creating a mental image of all the conversations that a chatbot should be aware of, together with what kind of expressions should trigger each conversation.
For example, we could train our chatbot to deal with two conversations to allow the users to ask for the office Address or Phone number.
Natural Language Processing is the ability of a computer program to understand human language as it is spoken.
In other words, NLP is the mechanism that allows chatbots—like NativeChat—to analyse what users say, extract essential information and respond with appropriate answers.
For example, if the user says: "When does your shop open?", the chatbot should be able to match this question to an opening-hours conversation, and respond with: "The shop opens from 9 AM to 5 PM".
What You are Going to Learn
The purpose of this article is to give you a general idea of what NLP is and how it works.
The idea is that with a better understanding of the NLP you should be able to train your chatbots to be able to understand their users a lot better.
There are many ways of handling each stage of the NLP processing, however, we will not go into too many details, as the aim of this article is to focus on the bigger picture.
The High-Level Understanding
From a high-level view, Natural Language Processing works in two stages:
NLP Model Training
In this stage, we (as the people that train our chatbots) provide our chatbots with a list of expressions that we would like them to associate with various conversations.
NLP takes all the training expressions and creates a model, which can be used to understand what the users say.
Process User Input and RespondNote, the model training needs to be done up-front before the chatbot starts interacting with the users.
Each time a user says something, NLP takes the user input and matches to the NLP Model, analyses the results and responds with an answer or follow-up question.
NLP Model Training
Before we let our chatbots interact with the users, we need to train the NLP Model. But what does that even mean?
This is like creating a mental image of all the conversations that a chatbot should be aware of, together with what kind of expressions should trigger each conversation.
ExampleIt is important to understand that the NLP Model is specific to the scenarios that it is meant to deal with. So, a banking chatbot should be able to understand and respond to Bank related requests (like: "How can I change the limit on my credit card?"), while a shopping chatbot should be able to deal with conversation helping a happy shopper.
For example, we could train our chatbot to deal with two conversations to allow the users to ask for the office Address or Phone number.
Training Questions | Response |