Skip to main content

Using Entities in Dialogflow to Extract Vital Information

Having learned about Intents in our previous blog, in this blog we will shed light on another interesting topic of Entities in Dialogflow.

Entities provide an efficient way to capture and extract crucial information like name, country, location and any custom information from user inputs.

Entities can be mainly categorized into three types:

1. System Entities: These are build-in entities provided by Dialogflow through which you can identify and extract most common type of data like name, country, date, email address etc.

2. Custom Entities: These are user-defined entities that are useful to extract custom data from the user inputs. For example, you can define a Fruit entity type that can identify and map different fruit types like Mango, Apple etc.

      3. Session Entities: As the name suggests, these entities remain active only for the session for which they have been created. They provide additional capability through which we can update custom entity types with new entries.

Using System Entities in Dialogflow

To use system entities, follow the steps below:

  1. Open the Dialogflow Console.
  2. Select the desired agent.
  3. Click the Intents option.

      

The Intents screen displays.

     4. Click the desired intent.
     


    5. Enter a desired training phrase.
          6. Double-click on any entity that you want to extract as system entity.
          A list of system entities display.
          
          

            7. Select the desired system entity.

            Now the desired name will be extracted as @sys.given-name:name.


You can now add as many training phrases and Dialogflow can easily train itself and extract the desired information matching with that system entity. In this way, you can add more training phrases and annotate entities to extract information without much efforts.

Creating Custom Entities in Dialogflow

To create custom entities, follow the steps below:

1. Click the Entities option.

2. Click the Custom tab. 

3. Click the Create Entity option.


      4. Enter the Entity name

    


      5. Enter the reference value in Enter reference value.

      6. Enter the synonyms associated with the particular reference value.

      Giving more number of synonyms ensures that the bot gets trained properly to identify particular entity in user input.

      7Click the Save button.

      8. Go to a desired intent.

      9. Enter the training phrase as shown figure below.

      Dialogflow automatically identifies the phrases and annotates them with desired entities.



There are also session entities, regexp entities, and fuzzy matching that provide more dynamic capability to extract various types of data from user input. We will further explore advanced topics in entities in our subsequent blogs.

Till that time, Happy Coding! 

Comments

  1. I got the new the king casino no deposit bonus【Malaysia】
    communitykhabar William】pinterest in 2021, the 출장샵 king herzamanindir casino apr casino no 토토 사이트 추천 deposit bonus,【WG98.vip】⚡,taylorlancer,taylorlancer,golfking.

    ReplyDelete
  2. Best No Deposit Bonus Codes in India - Herzamanindir.com
    5 steps1.Visit the official website of No Deposit India.
    Benefits of using a no deposit https://septcasino.com/review/merit-casino/ bonus.
    Benefits ventureberg.com/ of using a herzamanindir no deposit bonus.
    Benefits of https://tricktactoe.com/ using a no deposit bonus.
    Online Sincere Accessory หารายได้เสริม domain www.online-bookmakers.info

    ReplyDelete

Post a Comment

Popular posts from this blog

API Documentation Overview

 In this blog, we will explore an example of API documentation for one example function findNeedles(). findNeedles() Overview The purpose of the findNeedles() API takes two string parameters i.e. needles array and haystack and finds the total number of occurrences of each element in the needles array that are present in the haystack string. This function searches for only five words or less from the needles array and logs an error if more words are available in the needles array. The comparison is done by first splitting the haystack string using literals like backspace, tab space, single quote, and double quote etc into single elements and then looping over the needles array to find occurrences of these elements. Parameters This function takes following parameters: Name Type Description haystack String A string of any length needles String Array An array of words     Sample Code publi

Understanding Intents in Dialogflow

Dialogflow is a highly robust and dynamic natural language understanding platform that allows you to quickly build highly scalable conversational chatbots by leveraging the power of Artificial Intelligence (AI). You can easily create highly customizable chatbots for variety of channels like web applications, mobile applications, Facebook page, social media apps and much more. It provides seamless integration with variety of channels like Facebook, Viber, Telegram, Slack and much more. But in order to start working on building a chatbot yourself, lets connect the dots to get a clear picture of entire chatbot development process in Dialogflow. To create a chatbot, let us understand some of the nuances of the Dialogflow. What are Intents? Intents help Dialogflow ascertain the real intent or motive of the user messages. You can provide a set of certain training phrases that can help Dialogflow get trained to recognize similar such phrases and trigger a specific intent whenever such