Real-word error correction with trigrams: correcting multiple errors in a sentence.
Spelling correction is a fundamental task in text mining. In this study, we assess the real-word error correction model proposed by Mays, Damerau and Mercer and describe several drawbacks of the model. We propose a new variation which focuses on detecting and correcting multiple real-word errors in...
| Publicado en: | Language Resources & Evaluation Vol. 52; no. 2; pp. 485 - 503 |
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| Formato: | Artículo |
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Springer Nature
Jun2018
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=129593482&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 129593482 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 1574020X 179V jtl: Language Resources & Evaluation issn: 1574020X maglogo: N pubinfo: dt: Jun2018 vid: 52 iid: 2 pid: 237 pub: Springer Nature artinfo: ui: 129593482 10.1007/s10579-017-9397-4 ppf: 485 ppct: 18 formats: fmt: – @attributes: type: T – @attributes: type: P size: 624KB tig: atl: Real-word error correction with trigrams: correcting multiple errors in a sentence. aug: au: Dashti, Seyed MohammadSadegh affil: Department of Computer Engineering, Kerman Branch, Islamic Azad University, Kerman, Iran su: Error correction (Information theory) Spelling errors Text mining Pronunciation Grammar sug: subj: Error correction (Information theory) Spelling errors Text mining Pronunciation Grammar keyword: Context-sensitive Language model Real-word error Spelling correction ab: Spelling correction is a fundamental task in text mining. In this study, we assess the real-word error correction model proposed by Mays, Damerau and Mercer and describe several drawbacks of the model. We propose a new variation which focuses on detecting and correcting multiple real-word errors in a sentence, by manipulating a probabilistic context-free grammar to discriminate between items in the search space. We test our approach on the Wall Street Journal corpus and show that it outperforms Hirst and Budanitsky’s WordNet-based method and Wilcox-O’Hearn, Hirst, and Budanitsky’s fixed windows size method. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y custom: Language Resources & Evaluation is a copyright of Springer, 2018. All Rights Reserved. item: Language Resources & Evaluation holder: Springer Nature dt: @attributes: year: 2018 holdings: @attributes: islocal: N |
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