Introduction

There are currently various avenues to introduce structure to entities in order to detected patterns. Microsoft LUIS, Alexa Conversations and Rasa is the leaders in this regard.

Although the examples given in this article will be based on LUIS, the underlying principles employed can be gleaned and implemented in other platforms.

The basic premise is to create roles for an entity, and then establish a pattern in which those roles might be used. This pattern then serves as a guide for the Conversational AI platform to detect patterns in user utterances. And subsequently useful conversational data.

Machine Learned Entities were introduced to Microsoft LUIS November 2019. Entity decomposition is important for both intent prediction and for data extraction in terms of entities. This superseded some of the existing functionality within LUIS. …

Introduction

In most parts of the world, the first language or home language of people are not one of the major languages. The problem here is is that the big cloud Conversational AI providers only make provision for the major languages.

Introduction

The concept formed during the 1950’s by an American mathematician Stephen Cole Kleene. He formalized the description of a regular language.

Introduction

There are a number of entity types which are available across chatbot frameworks, one of these are lists.

Lists are less glamorous than ML entities, entities with roles or composed entities. However, lists serves a specific purpose and there will most probably always be an implementation use-case for it.

Lists are used when you are looking for specific entities within an user utterance. Ideal scenarios are when you have a finite list of items, which might include:

• Towns / Cities
• Postal Codes
• Products / Services
• Employees etc.

Normalization adds consistency to the result yielded… For instance entries like john, Johnny, John, Jonh can be all normalized to Jonathan. …

Introduction

Even though most NLU tools converge on the same basic principles for entities and intents, when it comes to developing the dialog various approaches are followed.

With creating the state machine or managing the dialog nodes, four distinct approaches are currently followed in the chatbot marketplace for dialog creation and management.

• Design Canvas
• Dialog Configuration
• Native Code
• ML Stories

Bot Framework Composer falls into the category of Dialog Configuration.

The holy grail is to have a composer or design environment where the call-flow can be designed graphically and simultaneously create executable code.

Or least a design which can be imported into a chatbot framework and executed. …