Data · dataset · 2026
Spell Error Corpus for Minang Language
Listed in Teesside University Research Data Repository
The Spell Error Corpus for Minang Language (SPEML) is a dataset designed to support spelling error detection and correction for Minangkabau, a low-resource language of Indonesia.
Description
The base corpus was derived from a publicly available Indonesian–Minangkabau parallel corpus in the minangNLP GitHub repository. From the aligned sentence pairs, only the Minangkabau column was extracted, yielding 16,372 sentences.
The texts were preprocessed through normalization, cleaning, and deduplication, resulting in 16,334 unique Minangkabau sentences. SPEML contains 164,662 misspelled word forms organized into seven error categories. Spelling errors were generated using rule-based procedures covering character insertion (1–3 characters), character deletion (1–3 characters), character substitution (1–3 characters), character transposition (single adjacent swap), punctuation errors, real-word errors, and loanword errors, followed by expert validation for linguistic plausibility.
Read the rest (1 more)
This dataset can be reused to train, test, and benchmark NLP models, including spell checkers and language models, for Minangkabau spelling error detection and correction, as well as to evaluate methods by error type and edit length.
Links
Where it is published
- DOI doi.org/10.17632/bb9pfvysbr.1 ↗
DOI / persistent id · from researchdata tees ac uk
Catalogue records · 1
- OAI-PMH record data.mendeley.com/oai?verb=GetRecord&metadataPrefix=oai_dc&identifier=oai%3Adata… ↗
metadata API · from researchdata tees ac uk
Topics
Provenance · 1 source records, 11 field assertions
| Source | Key | Last seen | Raw |
|---|---|---|---|
| Teesside University Research Data Repository | oai:data.mendeley.com/bb9pfvysbr.1 | 17 h ago | JSON v1 |
| Field | Assertion | Extractor | Evidence |
|---|---|---|---|
| access_level | source · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | |
| concepts[field].anzsrc:field:460208 | mapping · researchdata tees ac uk | vocabulary-mapper@1.0.0 | keywords['Natural Language Processing'] |
| concepts[field].anzsrc:field:461103 | mapping · researchdata tees ac uk | vocabulary-mapper@1.0.0 | keywords['Deep Learning'] |
| concepts[field].anzsrc:group:4611 | mapping · researchdata tees ac uk | vocabulary-mapper@1.0.0 | keywords['Machine Learning'] |
| concepts[field].local:field:computer-science-ai | mapping · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | |
| concepts[field].local:field:earth-environmental | mapping · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | |
| concepts[field].local:field:humanities | mapping · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | |
| description | source · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | /metadata/dc/description |
| license | source · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | /metadata/dc/rights |
| publication_date | source · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | |
| title | source · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | /metadata/dc/title |