Virtual Assistants Overview
Natural Language Processing (NLP)
Concepts and Terminology
Quick Start Guide
Accessing the Platform
Navigating the XO Platform
Building a Virtual Assistant
Help & Learning Resources
Release Notes
Current Version
Recent Updates
Previous Versions
Request a Feature
Conversation Designer
Dialog Tasks
Mock Scenes
Dialog Tasks
Navigate Dialog Tasks
Build Dialog Tasks
Node Types
Intent Node
Dialog Node
Dynamic Intent Node
GenAI Node
GenAI Prompt
Entity Node
Form Node
Confirmation Node
Message Nodes
Logic Node
Bot Action Node
Service Node
Webhook Node
Script Node
Process Node
Agent Transfer
Node Connections
Node Connections Setup
Sub-Intent Scoping
Entity Types
Entity Rules
User Prompts or Messages
Voice Call Properties
Knowledge AI
Knowledge Graph
Build a Knowledge Graph
Manage FAQs
Knowledge Extraction
Import or Export Knowledge Graph
Prepare Data for Import
Importing Knowledge Graph
Exporting Knowledge Graph
Auto-Generate Knowledge Graph
Knowledge Graph Analysis
Answer from Documents
Alert Tasks
Small Talk
Digital Skills
Digital Forms
Digital Views
Session and Context Variables
Context Object
Intent Discovery
NLP Optimization
ML Engine
Model Validation
FM Engine
KG Engine
Traits Engine
Ranking and Resolver
Training Validations
NLP Configurations
NLP Guidelines
LLM and Generative AI
Event Handlers
Contextual Memory
Contextual Intents
Interruption Management
Multi-intent Detection
Amending Entities
Default Conversations
Conversation Driven Dialog Builder
Sentinment Management
Tone Analysis
Default Standard Responses
Ignore Words & Field Memory
Test & Debug
Talk to Bot
Utterance Testing
Batch Testing
Conversation Testing
Conversation Testing Overview
Create a Test Suite
Test Editor
Test Case Assertion
Test Case Execution Summary
Health and Monitoring
NLP Health
Flow Health
Actions Overview
Azure OpenAI
Google Maps
Microsoft Graph
Open AI
Agent Transfer Overview
Custom (BotKit)
NiceInContact(User Hub)
Configure Tokyo and Lower versions
Configure Utah and Higher versions
External NLU Adapters
Dialogflow Engine
Test and Debug
Dashboard Filters
Overview Dashboard
Conversations Dashboard
Users Dashboard
Performance Dashboard
Custom Dashboards
Custom Meta Tags
Create Custom Dashboard
Create Custom Dashboard Filters
NLP Insights
Conversations History
Conversation Flows
Conversation Insights
Feedback Analytics
Usage Metrics
Containment Metrics
Universal Bots
Universal Bot Definition
Universal Bot Creation
Training a Universal Bot
Universal Bot Customizations
Enabling Languages
Manage Assistant
Team Collaboration
Plan & Usage
Usage Plans
Support Plans
Conversation Sessions
Multilingual Virtual Assistants
Get Started
Supported Components & Features
Manage Languages
Manage Translation Services
Multiingual Virtual Assistant Behavior
Feedback Survey
Masking PII Details
IVR Settings
General Settings
Assistant Management
Manage Namespace
Data Table
Table Views
App Definitions
Data as Service
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
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
Migrate External Bots
Google Dialogflow Bot
API Reference
API Introduction
API List
koreUtil Libraries
SDK Reference
SDK Introduction
SDK Security
SDK Registration
Web Socket Connect and RTM
Installing the BotKit SDK
Using the BotKit SDK
SDK Events
SDK Functions
SDK Tutorials
BotKit - Blue Prism
BotKit - Flight Search Sample VA
BotKit - Agent Transfer
Widget SDK Tutorial
Web SDK Tutorial
Introduction to Admin Console
Administration Dashboard
User Management
Add Users
Manage Groups
Manage Roles
Data Tables and Views
Assistant Management
Invite Users
Send Bulk Invites
Import User Data
Synchronize Users from AD
Security & Control
Using Single-Sign On (SSO)
Two-Factor Authentication (2FA)
Security Settings
Cloud Connector
  1. Home
  2. Docs
  3. Virtual Assistants
  4. Overview
  5. Natural Language Processing (NLP)

Natural Language Processing (NLP)


Key NLP concepts such as utterance, intent, or dialog task are discussed in the Virtual Assistants Overview.


Conversational VAs are all about enabling a machine to have natural conversations with users. The key for a conversational VA to understand human interactions lies in its ability to identify the user’s intention (Intent Detection), extract useful information (Entity Extraction), and map them to relevant actions or tasks (Dialog Task execution). 

NLP is the science of deducing the intention and related information from natural conversations. The conversation flow in virtual assistants passes through various Natural Language Understanding (NLU) engines and conversation engines before the VA decides upon action and response.

This article provides an overview of the NLP flow within a virtual assistant and shows how you, as a developer, can leverage its features to build an efficient and accurate VA.’s NLP Approach

The Experience Optimization (XO) Platform employs a multi-engine approach to natural language, which combines the following three models for optimal outcomes:

  1. Fundamental Meaning (FM): A computational linguistics approach that is built on ChatScript. The model analyzes the structure of a user’s utterance to identify each word by meaning, position, conjugation, capitalization, plurality, and other factors;
  2. Machine Learning (ML): uses state-of-the-art NLP algorithms and models for machine learning to enable VAs to be trained and to gradually improve their intelligence;
  3. Knowledge Graph Engine (KG): The Knowledge Graph helps you turn your static FAQ text into an intelligent and personalized conversational experience.

With its three-fold approach, the XO Platform enables you to accelerate the Natural Language Understanding (NLU) performance of the virtual assistant and achieve optimal accuracy with relatively less training data. automatically enables the trained NLP capabilities to all built-in and custom VAs, and powers the way they communicate, understand, and respond to a user request.

NLP Building Blocks

When a virtual assistant built on the XO Platform receives a user utterance, it is processed to identify the user intent, extract any additional information (entities), and then answer the user via a task execution. NLP is mostly concerned with the first two – intent detection and entity extraction.

Steps in a Conversation Flow

The Conversation Flow involves going through the following steps:

  1. NLP Analysis: The user utterance goes through a series of NLP engines for entity extraction and intent detection. (You can extend the out-of-the-box NLP functionality to use your own engine. You can install the Bot Kit SDK and easily integrate the virtual assistant with any 3rd party NLP engine. The output from the 3rd party NLP engine complements the outputs from thus adding to the efficiency and accuracy of the engine.) The engines provided by the XO Platform are as follows:
    • Fundamental Meaning Engine which breaks up the utterances based on the grammar constructs;
    • Machine Learning Engine which classifies individual words in the utterance, using an example-based, auto-learning training process;
    • Knowledge Collection Engine which mostly deals with FAQ type user queries. It can also be configured to trigger tasks in response to the user query;
    • Traits Engine which is a multiclass classifier and can identify multiple categories in user utterances thus aiding in refining user intent detection;
    • Small Talk Engine which adds human flavor to the conversations;
    • Ranking and Resolver to score the results from the above engines and rank them according to the set business rules, with the purpose of deciding on the winning intent.
  2. Task Execution: The winning intent along with the entities extracted then passes through the conversation engine for the actual task execution. This engine maintains the state or context of the conversation with information like user details, the previous intents requested by the user, and any other information as tagged by the business rules. This helps provide a near-human conversation experience. The conversation engine uses this state information along with the following conditions to accept or reject the intent identified by the NLU engines
    • Pre-conditions – if an intent has a set of predefined conditions configured and if any of these conditions are not satisfied the winning intent is then rejected. For example, a booking payment intent should have the payee details available.
    • Negative patterns capture the presence of a pattern that should not identify a particular intent. For example “I lost my bag, how do I retrieve it” should, not assume that the user wants to be provided with information about baggage, and instead attempt to track their bag, based on the presence of the phrase “lost my bag”
    • Event handling – events defined for a welcome message, sentiment analysis, etc
  3. Interruptions handling: Other conditions such as Interruption settings (to handle situations where another intent is identified during the course of an ongoing task) or Sentiment Analysis settings (user sounds angry and hence should be transferred to an agent) are crucial for the action to be taken.
  4. Response Generation: A response is generated and presented to the user based on the channel of interaction. The response could be a success message, information as requested by the user, prompt for missing information or message concerning their transfer to a human agent.

NLP Training

In the previous section, we have seen the NLP process of the virtual assistant, but it needs some training on your part to ensure that the process proceeds as per your requirements. So, how do you train a fully functional VA to achieve the best results? How do you make maximum use of the features above?

We will see some basic guidelines for NLP training in this section, before going into the details of each of the NLU engines.

The NLP Training Process

Morphology is the underlying principle behind NLP. Morphology is the study of words, how they are formed, and their relationship to other words in the same language. It analyzes the structure of words and parts of words, such as stems, root words, prefixes, and suffixes. Morphology also looks at parts of speech, intonation, and stress, and the ways the context can change a word’s pronunciation and meaning.

Based on this, a user utterance undergoes the following preprocessing before an attempt at entity extraction and intent detection:

  • Tokenization – Splitting of utterances into sentences (Sentence tokenization) and Splitting of Sentence(s) into words. NLP uses TreeBank Tokenizer for English. Each language might have its own tokenizer
  • toLower() – Convert all the text into lower (Not done for German, since the word meaning changes based on the case). This process is done only by ML and KG engines.
  • StopWord removal – Each language has its own set of stop words that can be edited by the developer. Removes words that may not contribute to improving the learning. This process is done only in ML and KG engines. This setting is optional, but is disabled by default.
  • Lemmatization or Stemming depending on the language 
    • Stemming – Retains the stem of the word like “Working”->” work”, “Running”->” Run”, “housing”->”hous”. It basically cuts the words. The output word may not be a valid language word
    • Lemmatization – Converts the word to its base form using the dictionary. Like in earlier examples “Working”->” work”, “Running”->” Run” however,  “housing”->” house”. 
  • N-grams – Helps in combining co-occurring words. For example, “New York City” and “Internet Explorer”. Each word has its own meaning. But when we take tri-gram in the first case and bi-gram in the second case, it actually results in a more meaningful word. N-grams also help in getting some context before or after a word.


The first step in NLP training is to define the scope of the VA, narrowing down the problem the Virtual Assistant will need to solve. This helps in configuring the various training nuances you will require. This involves brainstorming sessions with various stakeholders like SMEs/BAs, Conversation Experience Designers, VA Developers, NLP Analysts/Data Engineers, NLP Trainers, and Testers.

The basic guidelines we suggest to keep in mind while scoping the VA are the following:

  • Start with a problem to solve – get a clear idea of what the VA is supposed to accomplish. Talk to business analysts and VA developers to understand the requirements and the actual functionality of the Virtual Assistant.
  • Create a list of Intents – this will streamline the entire process
      • For each intent, identify the key results that the VA is aiming to accomplish;
      • The focus should be on the needs of the user, not the platform requirements.
  • Detail out example conversations – both user utterances and responses
      • Create user personas and think about the conversations they might engage in with your VA. 
      • Think through edge cases, follow-ups, and clarifying questions;
      • You can leverage the Storyboard feature of the platform if it has not been already used for the VA development phase.
  • Brainstorm what an end user might ask as part of achieving their intent – these would be the alternate utterances for every intent. Try to also include idioms and slang.’s NLU Engines and When to Use Them

We have seen above that the XO Platform offers three main engines for NLU:

  • Machine Learning (ML)
  • Fundamental Meaning (FM)
  • Knowledge Collection  / Knowledge Graph (KG)

When do you use each engine? Each of these engines has settings and configurations. Our article on Optimizing NLP to Improve VA Performance discusses this in more detail. Here we will list out some broad guidelines as to which engine to use when.

Machine Learning Engine

Machine Learning (ML) is the recommended engine for training a VA. The reason for this is its flexibility and auto-learn feature. Given a few examples, the engine learns and is capable of understanding similar new utterances. The training utterances need not be full sentences, as the ML can learn from phrases too.

If you have a large corpus for each intent that you are planning to implement, then use Machine Learning. If you don’t have a corpus it would be a good idea to develop one. In the long run, it is better to spend time building a large corpus and use ML rather than going for the other less time-consuming, easier options.

The way you define a large corpus could differ depending on the intents. For example, if the intentions are very different from each other and can be understood using their sample data like “Find Flight” or “Change Sear“, then a corpus of 200-300 for each intent is sufficient. However, if intents are closer to each other (and usually start with a similar utterance, such as “Change Seat” and “Change Flight”, then the corpus should be in 1000s.

Similarly, if you are planning to use Deep Neural Networks, you need a higher number of samples for better predictions of both True Positives and True Negatives, as these networks are data-hungry. Learn more about the ML Engine.

Knowledge Graph Engine

If your intents are more query-like in nature than transactional tasks or if the content is in documents and you want the VA to answer user queries from documents, then use Knowledge Collection. This engine can also be used to trigger dialog tasks in response to user queries thus incorporating other features available within the XO Platform.

If you have a lot of Intents and do not have time to prepare alternate utterances, but you are able to manually annotate some important terms, use Knowledge Collection. It is advisable to spend some time building a corpus and going for Machine Learning since annotation in the Knowledge Graph works in a similar way to ML’s auto-learning process. Learn more about the Knowledge Graph.

Fundamental Meaning Engine

If you have cases where users employ idiomatic sentences or command-like sentences or if you are not too strict about some false positives then use the Fundamental Meaning (FM) engine. Learn more about the FM Engine.

NLP Organization within the XO Platform

To get started optimizing your VA’s NLP, you need to select the VA you’re working with, then access  Build > Natural Language. The NLP 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.
  • 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 settings for user utterances and negative intent patterns.

Related Links

Previous Post
Virtual Assistants Overview

Next Post
Virtual Assistant Concepts and Terminology

Responsive Menu
Add more content here...
Responsive Menu
Add more content here...