GETTING STARTED
Kore.ai XO Platform
Virtual Assistants Overview
Natural Language Processing (NLP)
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Quick Start Guide
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Building a Virtual Assistant
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CONCEPTS
Design
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Supported Company Names
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Knowledge Graph
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Traits, Synonyms, and Stop Words
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Update Knowledge Graph
Introduction
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Introduction
Overview Dashboard
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Introduction
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Smart Bots
Universal Bots
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Enabling Languages
Store
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Plan & Usage
Overview
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Multilingual Virtual Assistants
Get Started
Supported Components & Features
Manage Languages
Manage Translation Services
Multiingual Virtual Assistant Behavior
Masking PII Details
Variables
Collections
IVR Settings
General Settings
Assistant Management
Manage Namespace
Data as Service
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HOW TOs
Build a Travel Planning Assistant
Travel Assistant Overview
Create a Travel Virtual Assistant
Design Conversation Skills
Create an ‘Update Booking’ Task
Create a Change Flight Task
Build a Knowledge Graph
Schedule a Smart Alert
Design Digital Skills
Configure Digital Forms
Configure Digital Views
Train the Assistant
Use Traits
Use Patterns
Manage Context Switching
Deploy the Assistant
Configure Agent Transfer
Use Bot Functions
Use Content Variables
Use Global Variables
Use Web SDK
Build a Banking Assistant
Design Conversation Skills
Create a Sample Banking Assistant
Create a Transfer Funds Task
Create a Update Balance Task
Create a Knowledge Graph
Set Up a Smart Alert
Design Digital Skills
Configure Digital Forms
Configure Digital Views
Add Data to Data Tables
Update Data in Data Tables
Add Data from Digital Forms
Train the Assistant
Composite Entities
Use Traits
Use Patterns for Intents & Entities
Manage Context Switching
Deploy the Assistant
Configure an Agent Transfer
Use Assistant Functions
Use Content Variables
Use Global Variables
Intent Scoping using Group Node
Analyze the Assistant
Create a Custom Dashboard
Use Custom Meta Tags in Filters
APIs & SDKs
API Reference
API Introduction
API List
API Collection
koreUtil Libraries
SDK Reference
SDK Introduction
SDK Security
SDK Registration
Web Socket Connect and RTM
Using the BotKit SDK
BotKit SDK Tutorial - Blue Prism
Widget SDK Tutorial
Web SDK Tutorial
ADMINISTRATION
Introduction to Admin Console
Administration Dashboard
User Management
Add Users
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Manage Roles
Assistant Management
Enrollment
Invite Users
Send Bulk Invites
Import User Data
Synchronize Users from AD
Security & Control
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Cloud Connector
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  1. Home
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  3. Virtual Assistants
  4. Natural Language
  5. Improving VA Performance – NLP Optimization

Improving VA Performance – NLP Optimization

A chatbot’s ability to consistently understand and interact with a user is dictated by the robustness of the Natural Language Processing (NLP) that powers the conversation.

Kore.ai’s platform uses a unique Natural Language Processing strategy, combining Fundamental Meaning and Machine Learning engines for maximum conversation accuracy with little upfront training. Bots built on Kore.ai’s platform can understand and process multi-sentence messages, multiple intents, contextual references made by the user, patterns and idiomatic sentences, and more. See here for an overview.

The NL engine includes recognition support for a wide range of entities and provides the tools needed to further customize your bot’s language understanding using additional patterns.

Optimizing your Virtual Assistant

To make sure your VA is NLP-optimized, you can define, and refine names and terms used for your assistant to enhance the NLP interpreter accuracy and performance to recognize the right bot task for the user.
You begin by defining synonyms at the task level, and then manage and refine synonyms, and test at the bot level.

To get started optimizing your Virtual Assistant and tasks, you need to access the Natural Language options. These options are categorized under various headings for your convenience:

  • Training – In the Training section, you can define how the NLP interpreter recognizes and responds to the user input for a VA, and then train the interpreter to recognize the correct user intent.
    • Machine Learning Utterances – With Machine Learning, you can enhance recognition of user utterances for better recognition and system performance for the user intent which is the intended task that the user wants to access.
    • Synonyms & Concepts – You can use the Synonyms section to optimize the NLP interpreter accuracy in recognizing the correct intent and entity provided by the user.
    • Patterns & Rules – In the Patterns section, you can define slang, metaphors, or other idiomatic expressions for intent and entities.
  • Thresholds & Configurations – In this section, you can define the recognition confidence levels required for minimum recognition actions, the confidence range for asking a user to choose from a list of possible matches, and a recognition confidence level for a positive match for the knowledge graph.
  • Modify Advanced Settings like auto training setting for user utterances and negative intent patterns.

You can start optimizing your Virtual Assistant, by:

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