Documentation Index Fetch the complete documentation index at: https://koreai.mintlify.app/llms.txt
Use this file to discover all available pages before exploring further.
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 (e.g., “Working” → “work”). Output may not be a valid word. Lemmatization Converts to base form using a dictionary (e.g., “housing” → “house”). N-grams Combines co-occurring words (e.g., “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.
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:
