Semantic Shift Type Classifier

Classify caller explicit semantic extensions by retained/lost/gained referents, distinguishing narrowing/broadening/overlap; not automatic metaphor, evaluative shift or historical-causation inference.

Description

Meaning extensions can be modeled as sets of referents.

Meaning extensions can be modeled as sets of referents. Gained and lost members distinguish broadening, narrowing and overlapping changes.^[1]

Enter nonempty distinct nonempty-string labels for old/new referents. Compare exact case-sensitive sets; return class and three counts.

When to use Semantic Shift Type Classifier

Use Semantic Shift Type Classifier when you need to evaluate the supplied records under the calculation and evidence rules below. Labels are supplied evidence. No metaphor, evaluation, etymology or historical causation is inferred.

Prepare explicit text tokens, annotated language observations, caller mappings or a supported locale as requested. Preserve source evidence and the stated Unicode counting convention.

How to use Semantic Shift Type Classifier and understand its results

Prepare explicit text tokens, annotated language observations, caller mappings or a supported locale as requested. Preserve source evidence and the stated Unicode counting convention.
Enter oldMeaningReferents, newMeaningReferents using the field table and calculation rules, then run the tool.
Read extensionalShiftClass, retainedReferentCount, lostReferentCount, gainedReferentCount. The explanation below defines the units, signs, ordering and any flags or placeholder values.
If the tool reports an input issue, correct the values and run it again. Apply the stated assumptions when interpreting a successfully returned result.

Input field	Meaning	Value type; see rules below	Requirement
oldMeaningReferents	Old meaning referents	list of string	Required
newMeaningReferents	New meaning referents	list of string	Required

Enter nonempty distinct nonempty-string labels for old/new referents. Compare exact case-sensitive sets; return class and three counts.

retained=intersection size; lost=old−retained; gained=new−retained

Units, indexing, boundary conditions and exceptional cases are defined in the calculation rules above.

Output field	Meaning	Value type
extensionalShiftClass	Extensional shift class	string
retainedReferentCount	Retained referent count	number
lostReferentCount	Lost referent count	number
gainedReferentCount	Gained referent count	number

json

{
  "input": {
    "oldMeaningReferents": [
      "sparrow"
    ],
    "newMeaningReferents": [
      "sparrow",
      "eagle"
    ]
  },
  "output": {
    "extensionalShiftClass": "broadening",
    "retainedReferentCount": 1,
    "lostReferentCount": 0,
    "gainedReferentCount": 1
  }
}

Limitations and assumptions

Labels are supplied evidence. No metaphor, evaluation, etymology or historical causation is inferred.

Numeric inputs, where present, must be finite. Follow the calculation-specific rules for overflow, underflow and exceptional cases. Numeric lists are limited to 10,000 entries; string lists to 128 strings of at most 128 characters each. The rules above may impose smaller limits. Missing required fields, extra fields, wrong value types and violations of the calculation rules are input errors.

Alternative or Complementary methods

Use Romance Latin Root Percent Estimate: An origin-share inventory needs separate denominators for classified and all observed weight. Unknown origin should remain visible. For a complementary task, use Language Family Tree Depth Lookup: A family-tree forest gives each node at most one parent. Depth and lineage depend on the caller’s explicit classification.

References

Semantic Shift Type Classifier — tool.io

Similar or alternative tools

Romance Latin Root Percent Estimate
An origin-share inventory needs separate denominators for classified and all observed weight. Unknown origin should remain visible.
Language Family Tree Depth Lookup
A family-tree forest gives each node at most one parent. Depth and lineage depend on the caller’s explicit classification.