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Back to NLP Topics Best practices for intent naming, ML training, patterns, synonyms, and concepts. Follow these guidelines before building or training your assistant.

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Intent Naming

Do:
  • Use 2-4 words: action verb + object + optional modifier. Example: Send Detailed Report, Get 3 Day Forecast.
  • Use the same verb across similar tasks: Show Issue / Show Report, not Show Issue / Get Report.
  • Use numerical form for digits: Get 3 Day Forecast.
Don’t:
  • Use single-word actions.
  • Use determiners (the, a, my, that) or pronouns.
  • Use platform terms: task, alert, action, cancel, discard, amend, webhook.
  • Use entity values in intent names (for example, avoid “Get Weather Today” where “Today” is an entity).
  • Use special characters: () & / \ $ [ ] + *
  • Use punctuation: - , . ! ? ' "
  • Use the app name or task type as part of the intent name.
  • Use the same word as both verb and noun across tasks (for example, Update Issue / Get Updates).
Dialog Tasks: Map to verb + object — “the goal is to Do Something.” Alerts: Map to object only (no verbs) — “alert me on: Status Updates.”

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ML Training

General:
  • Use batch test suites to compare ML models — there’s no fixed best configuration; iterate and compare.
  • Prepare utterances that are as diverse as possible.
  • Avoid noise (pleasantries); if unavoidable, spread it evenly across intents.
  • For large datasets, stop words and synonyms are auto-recognized by ML — explicit configuration isn’t required.
Using the Confusion Matrix:
  • Reassign utterances in False quadrants to the correct intent.
  • Improve cohesion by rephrasing or adding synonyms.
  • Avoid phrases that are similar across different intents.
Using K-fold Validation:
  • Ideal for large datasets (minimum 250 utterances total, 5 per intent).
  • Track Precision, Recall, and F1. Prioritize a higher Recall score.

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Patterns

Patterns match idiomatic or non-obvious expressions that synonyms can’t cover. Pattern matches take priority over synonym matches. General rules:
  • Use at minimum 3 words.
  • Use canonical forms: infinitive verbs, singular nouns.
  • Use lowercase; use US spelling.
  • Avoid determiners, pronouns, digits.
  • Don’t use elision (what's), special characters, or punctuation.

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Pattern Operators

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Entity Patterns

  • Include the positional wildcard * to mark where the entity appears.
  • Use surrounding words to delimit the entity search range.
  • Use < and > to mark sentence boundaries.
  • Don’t use field names or their synonyms in entity patterns.
  • Only the first instance with a valid value is extracted.
Example — Extracting ToFlight and FromFlight:
  • ToFlight pattern: to *1
  • FromFlight pattern: from * to and to from *1

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Negative Patterns

Negative patterns prevent false intent matches. Example: was trying to * — prevents triggering Book a Flight when the user says “I was trying to book a flight when I faced an issue.” To enable: Natural Language > NLU Config > Ranking and Resolver Engine > Negative Patterns.

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Synonyms

Use synonyms when the same concept can be expressed with different words. Synonyms are unidirectional (tool=bar ≠ bar=tool). Do:
  • Use canonical forms (infinitive verbs, singular nouns).
  • Use lowercase and US spelling.
  • Keep phrases under 5 words.
  • Use intent-driven synonyms: get is a synonym for show if the action means “find and display.”
Don’t:
  • Add synonyms to determiners, pronouns, or digits.
  • Use a synonym that could match conflicting tasks.
  • Use special characters or punctuation.
  • Assign synonyms to multiple words in a single entry.
  • Abbreviate to fewer than 2 letters.

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Synonym Match Types

Synonyms apply to both intent and entity identification. Entity identification is triggered only after an intent is matched.

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Concepts

Concepts group related terms under a single label, usable in patterns. Naming rules:
  • Must start with ~.
  • Allowed characters: a-z, A-Z, 0-9, _.
  • At least one letter after ~.
  • Can’t start or end with _.
  • Case-insensitive.
Valid: ~my_concept, ~Sample, ~test123 Invalid: ~_concept, ~concept_, ~a-concept, ~123test Use concepts in patterns: buy ~fruit matches buy orange, buy banana, etc. System concepts: Pre-built by the platform (for example, ~emohello, ~yes, ~no). Add the ~disable_help concept to disable the default help response.

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Standard Responses

Standard responses are template messages for common conversational situations: ambiguity resolution, authorization requests, confirmations, interruptions. Customize them at Natural Language > Standard Responses. Responses support:
  • Plain text or JavaScript for dynamic content.
  • Contextual tags replaced at runtime (for example, <list-of-tasks>).
  • Channel-specific formatting.

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Knowledge Graph — Key Considerations

The KG model adds domain intelligence on top of ML and FM. Assign FAQs to nodes based on key domain terms to improve disambiguation. Traits in KG: Use when FAQs are mutually exclusive in nature. For example, “booking process” vs. “booking issues” — creating separate traits ensures the engine doesn’t suggest booking process FAQs for issue-related queries. Use traits judiciously — overuse causes false negatives. Ensure:
  • Good trait coverage across utterances.
  • Traits are mutually exclusive where intended.
  • All relevant FAQs are tagged to the correct trait.