Bucking the trend: improved evaluation and annotation practices for ESL error detection systems.
The last decade has seen an explosion in the number of people learning English as a second language (ESL). In China alone, it is estimated to be over 300 million (Yang in Engl Today 22, ). Even in predominantly English-speaking countries, the proportion of non-native speakers can be very substantial...
| Publicado en: | Language Resources & Evaluation Vol. 48; no. 1; pp. 5 - 32 |
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| Autores principales: | , , |
| Formato: | Artículo |
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Springer Nature
Mar2014
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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=95006722&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 95006722 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 1574020X 179V jtl: Language Resources & Evaluation issn: 1574020X maglogo: N pubinfo: dt: Mar2014 vid: 48 iid: 1 pid: 237 pub: Springer Nature artinfo: ui: 95006722 10.1007/s10579-013-9243-2 ppf: 5 ppct: 27 formats: fmt: @attributes: type: P size: 534KB tig: atl: Bucking the trend: improved evaluation and annotation practices for ESL error detection systems. aug: au: Tetreault, Joel Chodorow, Martin Madnani, Nitin affil: Educational Testing Service, Princeton USA Hunter College of CUNY, New York USA su: English as a foreign language Annotations Learning Crowdsourcing Error detection (Information theory) sug: subj: English as a foreign language Annotations Learning Crowdsourcing Error detection (Information theory) keyword: Annotation Evaluation Grammatical error detection systems NLP ab: The last decade has seen an explosion in the number of people learning English as a second language (ESL). In China alone, it is estimated to be over 300 million (Yang in Engl Today 22, ). Even in predominantly English-speaking countries, the proportion of non-native speakers can be very substantial. For example, the US National Center for Educational Statistics reported that nearly 10 % of the students in the US public school population speak a language other than English and have limited English proficiency (National Center for Educational Statistics (NCES) in Public school student counts, staff, and graduate counts by state: school year 2000-2001, ). As a result, the last few years have seen a rapid increase in the development of NLP tools to detect and correct grammatical errors so that appropriate feedback can be given to ESL writers, a large and growing segment of the world's population. As a byproduct of this surge in interest, there have been many NLP research papers on the topic, a Synthesis Series book (Leacock et al. in Automated grammatical error detection for language learners. Synthesis lectures on human language technologies. Morgan Claypool, Waterloo ), a recurring workshop (Tetreault et al. in Proceedings of the NAACL workshop on innovative use of NLP for building educational applications (BEA), ), and a shared task competition (Dale et al. in Proceedings of the seventh workshop on building educational applications using NLP (BEA), pp 54-62, ; Dale and Kilgarriff in Proceedings of the European workshop on natural language generation (ENLG), pp 242-249, ). Despite this growing body of work, several issues affecting the annotation for and evaluation of ESL error detection systems have received little attention. In this paper, we describe these issues in detail and present our research on alleviating their effects. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y custom: Language Resources & Evaluation is a copyright of Springer, 2014. All Rights Reserved. item: Language Resources & Evaluation holder: Springer Nature dt: @attributes: year: 2014 holdings: @attributes: islocal: N |
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