Joint English Spelling Error Correction and POS Tagging for Language Learners Writing
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, with one measured rule per type.
For Mova
The chapter is 06. Spelling rules: inflection, derivation, compounds and national standards, with 05. The 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.
Cite
@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/}
}References
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