Natural Language Processing

Back to NLP Topics The Platform uses a multi-engine NLP approach to identify user intent, extract entities, and execute the correct task. Understanding NLP helps you train your VA for accurate intent detection and natural conversations.

NLP Engines

Engine	Description
Fundamental Meaning (FM)	Computational linguistics built on ChatScript. Analyzes word meaning, position, conjugation, capitalization, and sentence structure.
Machine Learning (ML)	Trains on example utterances; learns and generalizes to recognize similar inputs.
Knowledge Graph (KG)	Converts FAQ content into structured conversational responses.
Traits Engine	Multi-class classifier identifying characteristics in utterances to refine intent detection.
Small Talk Engine	Handles conversational pleasantries to make interactions feel natural.
Ranking and Resolver (R&R)	Scores and ranks results from all engines to determine the winning intent.

Conversation Flow

NLP Analysis — User input passes through all NLP engines for intent detection and entity extraction.
Task Execution — The winning intent executes. The conversation engine maintains state (user details, previous intents, context).
- Preconditions — If required conditions aren’t met, the intent is rejected.
- Negative patterns — Patterns that prevent incorrect intent matches.
- Event handling — Welcome messages, sentiment, etc.
Interruption Handling — Manages mid-task intent switches and sentiment-triggered agent transfers.
Response Generation — A response is generated and rendered for the user’s channel.

NLP Preprocessing

Before intent detection, each utterance undergoes:

Step	Description
Tokenization	Splits utterance into sentences, then words. Uses TreeBank Tokenizer for English.
toLower()	Converts to lowercase. Not applied to German (word meaning changes by case). ML and KG only.
Stop word removal	Removes low-signal words. Language-specific; disabled by default. ML and KG only.
Stemming	Cuts words to their stem (for example, “Working” → “work”). Output may not be a valid word.
Lemmatization	Converts to base form using a dictionary (for example, “housing” → “house”).
N-grams	Combines co-occurring words (for example, “New York City”) for richer context.

Choosing an NLP Engine

Scenario	Recommended Engine
Large corpus per intent	ML — flexible, auto-learns from examples. Corpus of 200-300 for distinct intents; 1000+ for similar intents.
Query/FAQ-type intents or document-based answers	KG — semantic matching for knowledge content.
Idiomatic, command-like sentences, or tolerance for false positives	FM — deterministic, rule-based.

NLP Configuration in the Platform

Go to Automation > Natural Language:

Section	Purpose
Training	Add ML utterances, synonyms, concepts, and patterns.
NLU Config	Set confidence thresholds, engine tuning, and advanced settings.

For engine-specific training and tuning, see:

​NLP Engines

​Conversation Flow

​NLP Preprocessing

​Choosing an NLP Engine

​NLP Configuration in the Platform

NLP Engines

Conversation Flow

NLP Preprocessing

Choosing an NLP Engine

NLP Configuration in the Platform