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

Proceedings of COLING 2012: Technical Papers, pages 2357–2374, COLING 2012, Mumbai, December 2012. Joint English Spelling Error Correction and POS Tagging for Language Learners Writing Keisuke Sakaguchi, Tomoya Mizumoto, Mamoru Komachi, Yuji Matsumoto Graduate School of Information Science Nara Institute of Science and Technology 8916-5, Takayama, Ikoma, Nara 630-0192, Japan {keisuke-sa, tomoya-m, komachi, matsu}@is.naist.jp Abstract We propose an approach to correcting spelling errors and assigning part-of-speech (POS) tags simultaneously for sentences written by learners of English as a second language (ESL). In ESL writing, there are several types of errors such as preposition, determiner, verb, noun, and spelling errors. Spelling errors often interfere with POS tagging and syntactic parsing, which makes other error detection and correction tasks very difficult. In studies of grammatical error detection and correction in ESL writing, spelling correction has been regarded as a preprocess- ing step in a pipeline. However, several types of spelling errors in ESL are difficult to correct in the preprocessing, for example, homophones (e.g. *hear/here), confusion (*quiet/quite), split (*now a day/nowadays), merge (*swimingpool/swimming pool), inflection (*please/pleased) and derivation (*badly/bad), where the incorrect word is actually in the vocabulary and gram- matical information is needed to disambiguate. In order to correct these spelling errors, and also typical typographical errors (*beggin- ning/beginning), we propose a joint analysis of POS tagging and spelling error correction with a CRF (Conditional Random Field)-based model. We present an approach that achieves sig- nificantly better accuracies for both POS tagging and spelling correction, compared to existing approaches using either individual or pipeline analysis. We also show that the joint model can deal with novel types of misspelling in ESL writing. Keywords: Part-of-Speech Tagging, Spelling Error Correction. 2357 1 Introduction Automated grammatical error detection and correction have been focused on natural language processing (NLP) over the past dozen years or so. Researchers have mainly studied English grammatical error detection and correction of areas such as determiners, prepositions and verbs (Izumi et al., 2003; Han et al., 2006; Felice and Pulman, 2008; Lee and Seneff, 2008; Gamon, 2010; Dahlmeier and Ng, 2011; Rozovskaya and Roth, 2011; Tajiri et al., 2012). In previous work on grammatical error detection and correction, spelling errors are usually corrected in a preprocessing step in a pipeline. These studies generally deal with typographical errors (e.g. *begginning/beginning). In ESL writing, however, there exist many other types of spelling er- rors, which often occur in combination with, for example, homophone (*there/their), confusion (*form/from), split (*Now a day/Nowadays), merge (*swimingpool/swimming pool), inflection (*please/pleased), and derivation (*badly/bad) errors. Unlike typographical errors, these spelling errors are difficult to detect because the words to be corrected are possible words in English. Previous studies in spelling correction for ESL writing depend mainly on edit distance between the words before and after correction. Some previous works for correcting misspelled words in native speaker misspellings focus on homophone, confusion, split, and merge errors (Golding and Roth, 1999; Bao et al., 2011), but no research has been done on inflection and derivation errors. One of the biggest …

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