Top Irregular English Past Tenses Lookup
Resolve bounded supported common English irregular past spellings with explicit was/were agreement and unchanged unknowns flagged; unlike existing regular-past tool, no guessed regularization, past participles or pronunciation of read.
Description
Irregular English past spellings require lexical information.
Irregular English past spellings require lexical information. Unknown verbs should remain distinguishable from correctly unchanged irregular forms.1
Enter nonempty lowercase ASCII verbs1–64 letters and exact singular/plural choice for be. Apply the supported 50-entry table, using was/were; return words and supported flags.
When to use Top Irregular English Past Tenses Lookup
Use Top Irregular English Past Tenses Lookup when you need to evaluate the supplied records under the calculation and evidence rules below. The explicit agreement choice applies to be and does not analyze subject agreement. No regularization, participles or pronunciation of read 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 Top Irregular English Past Tenses Lookup 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 baseVerbTokens, callerBePastAgreement using the field table and calculation rules, then run the tool.
- Read pastTenseTokensOrUnchangedUnknowns, supportedIrregularFlags. 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 |
|---|---|---|---|
| baseVerbTokens | Base verb tokens | list of string | Required |
| callerBePastAgreement | Caller be past agreement | string | Required |
Enter nonempty lowercase ASCII verbs1–64 letters and exact singular/plural choice for be. Apply the supported 50-entry table, using was/were; return words and supported flags.
known table entry→past spelling; unknown→original with flag0 Units, indexing, boundary conditions and exceptional cases are defined in the calculation rules above.
| Output field | Meaning | Value type |
|---|---|---|
| pastTenseTokensOrUnchangedUnknowns | Past tense tokens or unchanged unknowns | list of string |
| supportedIrregularFlags | Supported irregular flags | list of number |
{
"input": {
"baseVerbTokens": [
"go",
"eat",
"be"
],
"callerBePastAgreement": "singular"
},
"output": {
"pastTenseTokensOrUnchangedUnknowns": [
"went",
"ate",
"was"
],
"supportedIrregularFlags": [
1,
1,
1
]
}
}Limitations and assumptions
The explicit agreement choice applies to be and does not analyze subject agreement. No regularization, participles or pronunciation of read 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. Extremely large text inputs are rejected. Missing required fields, extra fields, wrong value types and violations of the calculation rules are input errors.
Alternative or Complementary methods
Use Kana-Rōmaji Mapping Lookup: Kana romanization depends on dictionary coverage and contextual conventions. A caller-supplied longest-match mapping makes its choices explicit. For a complementary task, use Semantic Shift Type Classifier: Meaning extensions can be modeled as sets of referents. Gained and lost members distinguish broadening, narrowing and overlapping changes.
References
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Top Irregular English Past Tenses Lookup — tool.io
Similar or alternative tools
- Kana-Rōmaji Mapping Lookup
Kana romanization depends on dictionary coverage and contextual conventions. A caller-supplied longest-match mapping makes its choices explicit.
- Semantic Shift Type Classifier
Meaning extensions can be modeled as sets of referents. Gained and lost members distinguish broadening, narrowing and overlapping changes.