implicature-intent 0.1.1

Detect implicit intent vs tone in short conversational turns

Implicature Intent

Detect implicit intent vs. tone in short conversational turns.

implicature-intent is a lightweight, zero-dependency Python library designed to separate what someone means (Intent) from how they say it (Tone). It is deterministic, explainable, and fast.

Why Intent ≠ Sentiment?

Sentiment analysis often conflates "tone" with "intent":

Response	Sentiment Tools Say	Actual Intent
"I'd love to, but I'm swamped."	Positive ("love")	NO
"Fine, I'll do it."	Negative	YES
"How about Tuesday instead?"	Neutral	Counter-offer
"I guess so, if I have to."	Negative	YES (reluctant)

This library disentangles these signals.

Features

• Zero Dependencies: Fast, heuristic-based scoring
• Explainable: Returns evidence phrases and confidence scores
• Actionable Output: Commitment level, needs_clarification flags, response types
• Offline-First: No API calls, no network required

Installation

pip install implicature-intent

Quick Start

from implicature_intent import analyze_intent

result = analyze_intent("I'd love to, but I have a conflict.")

print(result["orientation"])      # "NO"
print(result["sentiment_tone"])   # "positive"
print(result["intent_type"])      # "polite_reject"
print(result["mismatch"])         # True (tone contradicts intent)

Output Schema

{
    # Core Intent
    "orientation": "YES" | "NO" | "NON_COMMITTAL" | "UNKNOWN",
    "intent_type": "direct_accept" | "polite_reject" | "angry_agreement" |
                   "reluctant_yes" | "conditional_yes" | "counter_offer" |
                   "soft_deferral" | "delegation" | ...,

    # Response Classification
    "response_type": "direct" | "conditional" | "counter_offer" |
                     "deferral" | "delegation" | "question",

    # Sentiment & Tone
    "sentiment_tone": "positive" | "negative" | "neutral" | "mixed",
    "sentiment_score": -1.0 to 1.0,

    # Actionable Signals
    "commitment_level": 0.0 to 1.0,      # How firm is this response?
    "needs_clarification": bool,          # Should you follow up?
    "mismatch": bool,                     # Tone contradicts intent?

    # Politeness
    "politeness": "low" | "medium" | "high",
    "politeness_score": 0.0 to 1.0,

    # Explainability
    "confidence": 0.0 to 1.0,
    "evidence_phrases": list[str],
    "explanation": str
}

Examples

Detecting Hidden "No"

analyze_intent("Thanks for thinking of me, but I'm slammed this week.")
# → orientation: "NO", intent_type: "polite_reject", sentiment_tone: "positive"

Angry Agreement

analyze_intent("It's a stupid idea, but fine, I'll do it.")
# → orientation: "YES", intent_type: "angry_agreement", mismatch: True

Counter-Offer

analyze_intent("How about we push it to next Tuesday instead?")
# → orientation: "NO", response_type: "counter_offer", needs_clarification: True

Conditional Yes

analyze_intent("Sure, as long as we can wrap up by 5pm.")
# → orientation: "YES", response_type: "conditional", intent_type: "conditional_yes"

Low Commitment

analyze_intent("I'll try my best, hopefully I can make it work.")
# → orientation: "YES", commitment_level: 0.2, needs_clarification: True

Use Cases

• Sales CRM: Detect when a lead is politely declining vs. genuinely interested
• Customer Support: Identify frustrated agreement vs. satisfied resolution
• Meeting Scheduling: Parse conditional availability and counter-offers
• Chatbots: Understand user intent beyond keyword matching
• Email Analysis: Flag responses that need follow-up

Limitations

• Sarcasm: Hard to detect without audio/visual cues or deeper context
• Domain-Specific Language: "I'll table this" might be YES or NO depending on culture
• Very Short Responses: "Ok" is ambiguous without context

Development

# Install dev dependencies
pip install -e ".[dev]"

# Run tests
pytest

# Build for distribution
python -m build

Roadmap

• v0.2: Evaluation harness with benchmark dataset
• v0.3: Domain tuning (sales, support, scheduling)
• v0.4: Optional LLM adapter for complex cases

License

MIT

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Built by Loom Labs

Research Inspiration

This library is inspired by the research paper Disentangling Intent from Tone in Conversational Agents.