Chatlayer Documentation
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On this page
  • NLP model
  • Intents
  • Expressions
  • Entities

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  1. understand users
  2. Natural language processing (NLP)

Basic NLP concepts

This page covers the fundamental concepts of Natural language processing (NLP).

PreviousNatural language processing (NLP)NextDetect information with entities

Last updated 9 months ago

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Understanding natural language is challenging. It takes us over 12 years to learn 20,000 common words. Imagine how hard it is for computers! Training NLP engines requires massive data. Fortunately, pre-trained models help. Our NLP engine handles spelling errors, synonyms, slang, and word order.

In this page, you'll learn about the basics to learn your bot's NLP model.

NLP model

An NLP model is made of a set of and which are on data so that the model can recognize expressions that were never seen.

Each bot has its own NLP model.

You can set up your NLP model under the .

Whenever a user sends a message to the bot, the bot will check if that message can be labelled with an intent that is part of the NLP model.

Example

Intents

Example of intents

Some examples of intents:

  • Book train ticket

  • Talk to a human

  • Create support ticket

  • Greeting

  • Yes

Expressions

Expressions are example sentences to specific intent: they're all the different ways a user can express their intent.

Example of expressions

Here are a few expressions for the intent 'who are you'

  • Who are you?

  • What is your name?

  • Do you have a name?

  • Tell me more about yourself

  • Please, I'd like to know who I am talking to

  • How should I call you?

  • who is choo choo?

  • Tell me what your name is

  • Who are ya?

  • What do people call you?

Here are a few expressions for the intent 'order pizza'

  • I'd like to order some pizza please

  • Can I get a pizza to go?

  • I want a pizza margherita

  • I'd like to order some food

  • Can you help me order pizza?

  • I'm in the mood for some pizza tonight!

Please note that our NLP engine has a limit of 1000 characters. Messages with more than 1000 characters will always trigger the Not understood block.

Entities

For example, when a user types 'Get me a flight ticket,' the NLP will check if this sentence matches any of its expressions and check if this message contains similar words as the expressions. In the example above, the NLP gives a 93% confidence score that 'Get me a flight ticket' belongs to the intent 'Book flight'. Because this sentence is recognised above the , the response that is linked to this intent will be shown to the user.

An intent is a series of expressions (or utterances) that mean the same intention or goal from the user side. During the conversation, intents are recognised by the and serve to steer the conversation in different ways.

You can .

It is important to scope your intents well so the bot can recognise them more easily. Learn how to create good intents .

You can .

It's crucial for an intent to contain diverse expressions so that the NLP can give more accurate results. Learn more about how to create a good set of expressions .

are important pieces of information that can be extracted from an expression. You want to store these entities as variables so you can re-use them later on.

Chatlayer has different entity types. Learn all about them .

NLP threshold
NLP engine
here
Entities
here
trained
NLP tab
intents
entities
add expressions from the NLP tab
here
add intents from the NLP tab
The basics of NLP.
Expressions for a 'talk_to_human' intent.
Example of an expressions where @date, @origin, and @destination entities are extracted.