Pun Paronomasia Double Meaning Detector

Audit independently annotated construction evidence for Pun Paronomasia Double Meaning Detector. Feature columns, independently annotated in order: one expression, independently established two readings, context activates both. Unknown evidence and compatible classes retained; not automated semantic, historical, identity, legal or safety determination.

Description

A pun activates more than one reading of an expression in context.

A pun activates more than one reading of an expression in context.^[1]

The checker requires independently supported readings and context rather than inventing ambiguity from raw text.

When to use Pun Paronomasia Double Meaning Detector

Use this tool when you need to audit explicit construction annotations while keeping unknown features visible. Establish the language, clause boundaries, transcription basis, source context, or evidence annotations required by this particular tool.

How to use Pun Paronomasia Double Meaning Detector and understand its results

Establish the language, clause boundaries, transcription basis, source context, or evidence annotations required by this particular tool.
Enter observedFeatureFlags, featureKnownFlags, following the field table and calculation rules below, then run the tool.
Read result. Inspect the nested fields in the output table below and keep any missing evidence or selected indices visible.
Correct any input issues and run again. Before using a successful result, review its units, signs, evidence coverage, and stated model or reference assumptions.

Input field	Meaning	Value type	Requirement
observedFeatureFlags	Binary presence/absence annotations in the fixed feature-column order	list of number	Required
featureKnownFlags	Binary evidence-known flags; zero leaves that feature unknown	list of number	Required

Enter aligned binary observedFeatureFlags and featureKnownFlags in the exact fixed column order below. For each class, compare known features with its fixed rule: one requires presence, zero requires absence, and minus one ignores the column. A known mismatch excludes the class; an unknown required feature leaves it compatible but unwitnessed. Return all compatible classes, fully witnessed classes, and zero-based unknown-feature indices. A fully witnessed candidate satisfies only these annotations, not a broader semantic or intent test.

compatible_c=∀j(rule_cj=−1 or known_j=0 or feature_j=rule_cj); witnessed_c=∀j(rule_cj=−1 or (known_j=1 and feature_j=rule_cj))

Read these symbols with the field meanings and branch or reference rules above.

Fixed feature column order: one expression, independently established two readings, context activates both.

Candidate label	Fixed feature rule
pun-candidate	[1,1,1]

Output field	Meaning	Value type
result	Object containing the fields below	object
result.compatibleClasses	Candidates with no known feature contradiction; missing evidence may remain	list
result.witnessedClasses	Candidates with every required feature known and satisfied	list
result.unknownFeatureIndices	Zero-based feature columns whose known flag is zero	list

json

{
  "input": {
    "observedFeatureFlags": [
      1,
      1,
      1
    ],
    "featureKnownFlags": [
      1,
      1,
      1
    ]
  },
  "output": {
    "result": {
      "compatibleClasses": [
        "pun-candidate"
      ],
      "witnessedClasses": [
        "pun-candidate"
      ],
      "unknownFeatureIndices": []
    }
  }
}

Limitations and assumptions

Annotations and their interpretation must be established independently. Multiple candidates may remain, including candidates with missing evidence. This does not parse raw language, identify intention, prove argument soundness, or infer a rhetorical effect.

Missing required fields, unknown fields, wrong types, and violated calculation rules return input issues. Numeric values must be finite; nonfinite numeric outputs are rejected. Arrays must be dense, with no missing cells. Numeric lists allow at most 10000 entries, subject to smaller tool-specific bounds above. Extremely large composite outputs are rejected.

Alternative or Complementary methods

For a related task, use Antanaclasis Shifted Meaning Repeat Detector: Antanaclasis repeats an expression while shifting its sense. The checker needs independently supported repetition and different meanings rather than guessing a semantic change. For a complementary step, use Zeugma One Verb Two Objects Yoking Detector: Zeugma links one governing expression to multiple objects or applications. This checker reviews supplied verb, object, and semantic annotations, so its candidate result depends on evidence already established by the user.

References

Pun Paronomasia Double Meaning Detector — tool.io

Similar or alternative tools

Antanaclasis Shifted Meaning Repeat Detector
Antanaclasis repeats an expression while shifting its sense. The checker needs independently supported repetition and different meanings rather than guessing a semantic change.
Zeugma One Verb Two Objects Yoking Detector
Zeugma links one governing expression to multiple objects or applications. This checker reviews supplied verb, object, and semantic annotations, so its candidate result depends on evidence already established by the user.