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# Joint English Spelling Error Correction and POS Tagging for Language Learners Writing

**Authors:** [Keisuke Sakaguchi](https://md.mova.elamur.ai/free/en/docs/papers/authors/keisuke-sakaguchi/), [Tomoya Mizumoto](https://md.mova.elamur.ai/free/en/docs/papers/authors/tomoya-mizumoto/), [Mamoru Komachi](https://md.mova.elamur.ai/free/en/docs/papers/authors/mamoru-komachi/) and [Yuji Matsumoto](https://md.mova.elamur.ai/free/en/docs/papers/authors/yuji-matsumoto/) · **Year:** 2012 · **Venue:** Proceedings of COLING 2012: Technical Papers, pages 2357–2374 **Link:** [aclanthology.org/C12-1144](https://aclanthology.org/C12-1144/) · **Licence:** [CC BY-NC-SA 3.0](https://creativecommons.org/licenses/by-nc-sa/3.0/) (ACL Anthology, before 2016) — summary only; the CLC FCE and KJ learner corpora are for evaluation only

[**3** citations in our library](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/sakaguchi-2012-joint-english-spelling-error-correction/) of the 140673 papers whose bibliographies we read[Proceedings of COLING 2012](https://aclanthology.org/volumes/C12-1/)[licence](https://creativecommons.org/licenses/by-nc-sa/3.0/)

- [Publisher's page](https://aclanthology.org/C12-1144/)
- [PDF](https://aclanthology.org/C12-1144.pdf)

**Authors (4)**

1. Keisuke Sakaguchi
2. Tomoya Mizumoto
3. Mamoru Komachi
4. Yuji Matsumoto

Citations and author metrics from OpenAlex (); authors with the same name are not always told apart.

## What it is about

Spelling correction is usually a pipeline step before tagging, but it cannot fix learner errors that produce real words (homophones, confusions, split and merge errors, inflection and derivation errors such as "badly" for "bad"), and the tagger needs the context to choose the fix. The authors use a Japanese-style lattice tagger (MeCab, a CRF) that jointly chooses a path of words and tags. A misspelling lexicon adds each error with a cost, a flag and its correct form. On the Cambridge Learner Corpus FCE data (2,488 essays) the joint model raises tagging F-value from 0.960 to 0.981, against 0.968 for the pipeline. On the Konan-JIEM corpus the gain is 3.8 points. Spelling correction F-value rises by 5.0 points, with recall up 19.5 points and precision down from 0.519 to 0.445. Of the 8,349 spelling errors in the FCE data, 58.2% are typographical and 22.9% derivation errors.

## Ideas

- I1. `en/src/dict.rs`: a misspelling table in the same shape as the paper's, with the surface form, the correct form, a flag and a cost, built from training-set pairs only, so the held-out learner set stays held out. Report exact-match correction accuracy by error type.
- I2. Candidate choice by context: for a misspelling with several candidates ("analysys" to analysis or analyses) pick the candidate whose tag agrees with the tag of the context, using the tagger in `en/src/tag.rs`. Each choice records which tag decided it.
- I3. Report the precision cost: the paper gains recall and loses precision. Mova should show both, with a cost threshold above which a correction is not made, so a wrong correction costs more than a missed one.
- I4. Use the error-type table (typographical, homophone, confusion, split, merge, inflection, derivation) as the chapter structure of [06. Spelling rules: inflection, derivation, compounds and national standards](https://md.mova.elamur.ai/free/en/docs/languages/en/06-spelling-rules/), with one measured rule per type.

## For Mova

The chapter is [06. Spelling rules: inflection, derivation, compounds and national standards](https://md.mova.elamur.ai/free/en/docs/languages/en/06-spelling-rules/), with [05. The writing system](https://md.mova.elamur.ai/free/en/docs/languages/en/05-writing-system/) for characters. The code points are `en/src/dict.rs` (the misspelling entries), `en/src/tok.rs` (tokens that are real words but wrong in context) and `en/src/tag.rs` (the context tag for candidate choice). The measure: correction precision and recall per error type on a held-out learner set that Mova owns, with and without the threshold, run twice for determinism.

Topics: [Part-of-speech tagging](https://md.mova.elamur.ai/free/en/docs/papers/topics/part-of-speech-tagging/)

## Cite

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

```
@inproceedings{sakaguchi2012joint,
  title = {{Joint English Spelling Error Correction and POS Tagging for Language Learners Writing}},
  author = {Sakaguchi, Keisuke and Mizumoto, Tomoya and Komachi, Mamoru and Matsumoto, Yuji},
  booktitle = {Proceedings of COLING 2012},
  year = {2012},
  url = {https://aclanthology.org/C12-1144/}
}
```

- [← previous A semiautomatic lemmatisation procedure for treebanks. Old English strong and weak verbs](https://md.mova.elamur.ai/free/en/docs/papers/saenz-2017-semiautomatic-lemmatisation-procedure-treebanks-old/)
- [next → Disentangling Indirect Answers to Yes-No Questions in Real Conversations](https://md.mova.elamur.ai/free/en/docs/papers/sanagavarapu-2022-disentangling-indirect-answers-yes-no/)
- [↑ back to English](https://md.mova.elamur.ai/free/en/docs/papers/en/)

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