Antanaclasis Shifted Meaning Repeat Detector
Audit independently annotated construction evidence for Antanaclasis Shifted Meaning Repeat Detector. Feature columns, independently annotated in order: same lexical expression repeated, independently different senses. Unknown evidence and compatible classes retained; not automated semantic, historical, identity, legal or safety determination.
Description
Antanaclasis repeats an expression while shifting its sense.
Antanaclasis repeats an expression while shifting its sense.1
The checker needs independently supported repetition and different meanings rather than guessing a semantic change.
When to use Antanaclasis Shifted Meaning Repeat 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 Antanaclasis Shifted Meaning Repeat 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: same lexical expression repeated, independently different senses.
| Candidate label | Fixed feature rule |
|---|---|
| antanaclasis-candidate | [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 |
{
"input": {
"observedFeatureFlags": [
1,
1
],
"featureKnownFlags": [
1,
1
]
},
"output": {
"result": {
"compatibleClasses": [
"antanaclasis-candidate"
],
"witnessedClasses": [
"antanaclasis-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 Pun Paronomasia Double Meaning Detector: A pun activates more than one reading of an expression in context. The checker requires independently supported readings and context rather than inventing ambiguity from raw text. For a complementary step, use Antimetabole Reversal Repetition Checker: Antimetabole repeats words in reversed order across expressions. An exact lexical reversal differs from a semantic chiasmus whose roles may be expressed with different words.
References
Similar or alternative tools
- Pun Paronomasia Double Meaning Detector
A pun activates more than one reading of an expression in context. The checker requires independently supported readings and context rather than inventing ambiguity from raw text.
- Antimetabole Reversal Repetition Checker
Antimetabole repeats words in reversed order across expressions. An exact lexical reversal differs from a semantic chiasmus whose roles may be expressed with different words.