hexabot/api/docs/nlp

NLP Block Scoring

Purpose

NLP Block Scoring is a mechanism used to select the most relevant response block based on:

• Matching patterns between user input and block definitions
• Configurable weights assigned to each entity type
• Confidence values provided by the NLU engine for detected entities

It enables more intelligent and context-aware block selection in conversational flows.

Core Use Cases

Standard Matching

A user input contains entities that directly match a block’s patterns.

Example: Input: intent = enquiry, subject = claim
Block A: Patterns: intent: enquiry, subject: claim
Block A will be selected.

High Confidence, Partial Match

A block may match only some patterns but have high-confidence input on those matched ones, making it a better candidate than others with full matches but low-confidence entities. Note: Confidence is multiplied by a pre-defined weight for each entity type.

Example:
Input: intent = issue (confidence: 0.92), subject = claim (confidence: 0.65)
Block A: Pattern: intent: issue
Block B: Pattern: subject: claim
➤ Block A gets a high score based on confidence × weight (assuming both weights are equal to 1).

Multiple Blocks with Similar Patterns

Input: intent = issue, subject = insurance
Block A: intent = enquiry, subject = insurance
Block B: subject = insurance
➤ Block B is selected — Block A mismatches on intent.

Exclusion Due to Extra Patterns

If a block contains patterns that require entities not present in the user input, the block is excluded from scoring altogether. No penalties are applied — the block simply isn't considered a valid candidate.

Input: intent = issue, subject = insurance
Block A: intent = enquiry, subject = insurance, location = office
Block B: subject = insurance, time = morning
➤ Neither block is selected due to unmatched required patterns (`location`, `time`)

Tie-Breaking with Penalty Factors

When multiple blocks receive similar scores, penalty factors can help break the tie — especially in cases where patterns are less specific (e.g., using Any as a value).

Input: intent = enquiry, subject = insurance

Block A: intent = enquiry, subject = Any
Block B: intent = enquiry, subject = insurance
Block C: subject = insurance

Scoring Summary:
- Block A matches both patterns, but subject = Any is considered less specific.
- Block B has a redundant but fully specific match.
- Block C matches only one pattern.

➤ Block A and Block B have similar raw scores.
➤ A penalty factor is applied to Block A due to its use of Any, reducing its final score.
➤ Block B is selected.

How Scoring Works

Matching and Confidence

For each entity in the block's pattern:

• If the entity matches an entity in the user input:
  • the score is increased by: confidence × weight
    • Confidence is a value between 0 and 1, returned by the NLU engine.
    • Weight is a configured importance factor for that specific entity type.
• If the match is a wildcard (i.e., the block accepts any value):
  • A penalty factor is applied to slightly reduce its contribution: confidence × weight × penaltyFactor. This encourages more specific matches when available.

Scoring Formula Summary

For each matched entity:

score += confidence × weight × [optional penalty factor if wildcard]

The total block score is the sum of all matched patterns in that block.

Penalty Factor

The penalty factor is a global multiplier (typically less than 1, e.g., 0.8) applied when the match type is less specific — such as wildcard or loose entity type matches. It allows the system to:

• Break ties in favor of more precise blocks
• Discourage overly generic blocks from being selected when better matches are available