---
title: "Portable Spelling Corrector for a Less-Resourced Language:"
description: "Rule-based spellcheckers are hard to build and maintain for such morphology, so the authors use a… 37 references, cited by 5 papers here."
authors: ["Andargachew Mekonnen Gezmu", "Andreas Nürnberger", "Binyam Ephrem Seyoum"]
year: 2018
venue: "Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)"
source: "https://aclanthology.org/L18-1651/"
licence: "CC BY-NC 4.0 (ELRA, LREC proceedings)"
licence_url: "https://creativecommons.org/licenses/by-nc/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/gezmu-2018-portable-spelling-corrector-less-resourced/"
---
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# Portable Spelling Corrector for a Less-Resourced Language: Amharic

**Authors:** [Andargachew Mekonnen Gezmu](https://md.research.elamur.ai/free/en/authors/andargachew-mekonnen-gezmu/), [Andreas Nürnberger](https://md.research.elamur.ai/free/en/authors/andreas-nurnberger/) and [Binyam Ephrem Seyoum](https://md.research.elamur.ai/free/en/authors/binyam-ephrem-seyoum/) · **Year:** 2018 · **Venue:** [Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)](https://md.research.elamur.ai/free/en/venues/lrec/) ([LREC 2018](https://md.research.elamur.ai/free/en/venues/lrec/2018/)) · **Publisher:** [European Language Resources Association (ELRA)](https://md.research.elamur.ai/free/en/publishers/european-language-resources-association-elra/) · **Licence:** [CC BY-NC 4.0 (ELRA, LREC proceedings)](https://creativecommons.org/licenses/by-nc/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/L18-1651/) · [PDF](https://aclanthology.org/L18-1651.pdf) · [OpenAlex](https://openalex.org/W2805294584)

**Citations:** 6 citations (OpenAlex, 2026-10-14) · [5](https://md.research.elamur.ai/free/en/papers/gezmu-2018-portable-spelling-corrector-less-resourced/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
The paper tackles non-word spelling correction for Amharic, a morphologically rich Semitic language with a syllabic script and few NLP resources. Rule-based spellcheckers are hard to build and maintain for such morphology, so the authors use a corpus-driven noisy channel corrector. A word is flagged as misspelled when it is missing from a term list built from frequent corpus words. Candidates within Damerau-Levenshtein distance two are then ranked by two components: a trigram word language model with modified Kneser-Ney smoothing, which supplies context, and an error model over keystroke substrings. The error model learned from English typos is reused for Amharic. This works because Amharic is typed on a QWERTY keyboard through a modified SERA transliteration. The authors built a cleaner Contemporary Amharic Corpus (CACO) from proofread sources and compared it with the web-crawled HaBiT corpus. On both Amharic and English test data, the system beat GNU Aspell and Hunspell. The method needs little language-specific work, so it is a practical recipe for other low-resource languages typed on QWERTY keyboards.

## Key points
- The authors compiled CACO, roughly 18.9M tokens and 1.34M sentences. Sources include newspapers, magazines, fiction, history and politics books, children's books, ELRA-W0074 legal and news texts, Ethiopian News Agency news, and a contemporary Amharic Bible. HaBiT (about 17.6M tokens) and Crúbadán (about 6M tokens) were judged to contain too many typos.
- The English language model was trained on the BNC (about 97M tokens), preprocessed the same way as the Amharic data.
- The language model is a word trigram model with modified Kneser-Ney smoothing. The authors chose word-based over morpheme-based modelling, following earlier Amharic results.
- Preprocessing has several steps:
  - Amharic is transliterated with a modified SERA, with simpler codes for labiovelars and independent vowels.
  - Homophonic characters are merged, following the Ethiopian Languages Academy reform, which acts like case folding.
  - Numbers become a `#` placeholder and hyphenated words are split.
  - Sentences containing corpus hapaxes are dropped.
- When no candidate correction exists, the input is split into several words. This repairs run-on strings caused by missing spaces.

## Interesting ideas
- Detect errors with a term list drawn from the most frequent words in a clean corpus rather than a handcrafted lexicon; this automatically covers names, rare terms and neologisms.
- Treat words that appear only once in a corpus as likely typos, and remove whole sentences containing them before training.
- If no correction lies within the edit-distance budget, try segmenting the string into several known words. This is a simple fallback for missing spaces.
- Reuse an error model across languages by mapping both onto the same keyboard keystrokes, so typo statistics transfer without new annotation.
- Fold orthographic variants that sound identical into one canonical form before modelling, much like lowercasing.

## For Mova

How these ideas could serve Mova, our small open language model — its files, stages and tests:

- **I1** (tokens): The en tokenizer could flag out-of-vocabulary tokens against a frequency-derived list, and keep a traceable reason ("not in top-N corpus terms") for each flag.
- **I2** (learn): Silver-data pipelines could use the same cheap filter to discard noisy sentences before witness parsers vote on them.
- **I3** (tokens): The tokenizer could add an explainable split step for glued tokens in noisy input, before tagging and parsing.
- **I4** (learn): Keystroke-level typo models trained once could generate synthetic misspellings to stress-test the tagger and parser on noisy English.
- **I5** (tokens): Normalizing English spelling variants (British/American, archaic forms) before lemmatization would keep lemma counts consistent while the original form stays recorded.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

## Institutions

- [Addis Ababa University](https://md.research.elamur.ai/free/en/universities/addis-ababa-university/) — University, Addis Ababa, Ethiopia · 15 works · 77 citations
- [Otto-von-Guericke-Universität Magdeburg](https://md.research.elamur.ai/free/en/universities/otto-von-guericke-universitat-magdeburg/) — University, Magdeburg, Germany · 3 works · 125 citations

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🧭 Topics (2): [Semitic languages](https://md.research.elamur.ai/free/en/topics/semitic-languages/) · 2,908 works · [🌱 Low-resource languages](https://md.research.elamur.ai/free/en/topics/low-resource-languages/) · 267 works

## Cite

Cite as: Andargachew Mekonnen Gezmu, Andreas Nürnberger and Binyam Ephrem Seyoum. 2018. Portable Spelling Corrector for a Less-Resourced Language: Amharic. Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018).

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{gezmu2018portable,
  title = {{Portable Spelling Corrector for a Less-Resourced Language: Amharic}},
  author = {Andargachew Mekonnen Gezmu, Andreas Nürnberger and Binyam Ephrem Seyoum},
  booktitle = {Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)},
  year = {2018},
  doi = {10.63317/5i63t7yj84au},
  url = {https://doi.org/10.63317/5i63t7yj84au}
}
```

- [← previous Misogyny Detection and Classification in English Tweets: The Experience of the ITT Team](https://md.research.elamur.ai/free/en/papers/shushkevich-2018-misogyny-detection-classification-english-tweets/)
- [next → THU_NGN at SemEval-2018 Task 2: Residual CNN-LSTM Network with Attention for English Emoji Prediction](https://md.research.elamur.ai/free/en/papers/wu-2018-thu-ngn-semeval-2018-task-s18-1063/)
- [↑ back to papers of 2018](https://md.research.elamur.ai/free/en/papers/2018/page/6/)

## References

37 references, 6 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

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*Updated 1 November 2026*