Arabic Error Analysis
Contents |
Kassim Shaaban Second Language Acquisition English 345 January 2003 Click here to get it as a Word Document (Original Form) ___________________________________________________________ Table of Contents A. Introduction: 1. Importance how to do error analysis in english of communication 2. Communicative competence 3. Oral & Written communication 4. Emphasis on
Five Stages Of Error Analysis
written communication, the more difficult component 5. Native Speakers of English vs. Non-Native Speakers (Arabic speakers) 6. Mistake vs. Error error analysis in english language teaching => self-correctability factor 7. Rationale of study: Problems relating to the teaching of English as a First language to Arabic speakers are understudied. 8. Aim of paper: Attempt to
Types Of Error Analysis In Linguistics
identify, describe, categorize, and diagnose Arabic speakers’ errors in English essay writing + suggest some solutions. 9. Research Question: Is negative L1 transfer/interference the major cause for errors in the English writings of Brevet students? B. Theoretical Background: 1. Error Analysis 2. Models for Error Analysis 3. Sources of Errors C. Procedures: 1. Error/Data Collection 2. Error Identification and Categorization error analysis in english writing & Taxonomy Adopted D. Results E. Implications F. Limitations & Suggestions for future research G. Conclusion H. References I. Appendixes: 1. General List of Abbreviations 2. School mission statement (soon) 3. Student Survey 4. Arabic: General Language Characteristics 5. ESL Tip Sheet 1: Arabic (Sofer & Raimes, 2002) 6. Table 1: Error Categories & Sources + Abbreviations 7. Table 2: Categorizing & Diagnosing Errors 8. Table 3: Categorizing & Diagnosing Errors- Interlingual / Transfer Errors 9. Table 4: Categorizing & Diagnosing Errors- Intralingual / Dev. errors 10. Table 5: Error Sources & Numbers Compared 11. Oral & Written Presentations: Guidelines & Expectations 12. Error Analysis Exercises & Objective Tests for Remediation _______________________________________________________________________ Introduction “My purpose in being Part in the goverment is Change. I want to change the world. Change in the means of War, Freedom, and equalness. Also as being part of the government, One should have self-confidence, can stand-up for self, and others, and at the same time have a degree in a great proffession.Then How am I working now, I’am participation in every activity Possible, that has to do with improving our community today. On top of
PagesSTUDY ON SECOND LANGUAGE LEARNERS OF ARABIC: AN ERROR ANALYSIS APPROACH Uploaded byJassem JassemFiles1of error analysis 2Ph.D.thesis_1_JAJssem.pdfPh.D_thesis_0_JAJssem.pdfViewsconnect to downloadGetpdfREAD PAPERSTUDY ON SECOND LANGUAGE LEARNERS OF ARABIC: AN ERROR ANALYSIS APPROACH DownloadSTUDY ON SECOND LANGUAGE LEARNERS OF ARABIC: AN ERROR ANALYSIS APPROACH Uploaded byJassem error analysis in JassemLoading PreviewSorry, preview is currently unavailable. You can download the paper by clicking the button above.Loading PreviewSorry, preview is currently unavailable. You can download the paper by clicking the button above.GET pdf ×CloseLog InLog InwithFacebookLog InwithGoogleorEmail:Password:Remember me on this computerorreset passwordEnter the email address you signed up with and we'll email you a reset link.Need an account?Click here to sign up Job BoardAboutPressBlogPeoplePapersTermsPrivacyCopyrightWe're Hiring!Help Center Find new research papers in:PhysicsChemistryBiologyHealth SciencesEcologyEarth SciencesCognitive ScienceMathematicsComputer Science Academia © 2016
From MT Systems across Genres (using Arabic) Introduction Developers, researchers and end-users of Machine Translation (MT) systems are often interested in analysing their efficacy to establish the relative benefits of their application to translation. In other words, users of MT are interested in the quality of a system's output and whether it produces ‘good' translations. When it comes to judging the quality of MT performance, the basic tenant is captured in the maxim adopted by Papineni et al., ‘The closer a machine translation to a professional human translation, the better it is' (2001: 1). Numerous evaluative methods of judging MT system performance have been developed, and they fall into two broad categories; human evaluation and automatic evaluation. The most widely recognized benchmark for assessing MT quality is professional human translators who make judgements based on standards of accuracy, fidelity and fluency usually against ‘gold standard' human-translated reference texts (King, 1996 in Pryzybocki et al. 2006: 1). For the purpose of this investigation, MT output quality is seen from the perspective of the typical non-commercial end-user who may be one of the millions who use MT systems on a casual basis. These causal end-users are most likely to utilize MT for informational purposes or to ‘get the gist' of a text and in such instances it is accuracy of semantic content that becomes the major aspect of quality (Koponen, 2010: 2). This report will adopt an error analysis scheme to classify and count errors in texts machine translated by the popular MT systems Systran and Google Translate. The error scheme focuses on semantic errors found in the target texts. Two fields or text genres have been used; general politics and technology although both texts are designed for general rather than technical or specialist audiences. The results demonstrate how both MT systems perform and whether patterns across text genres may be suggested. Finally, the results will be compared to scores generated by the automated evaluation metric BLEU to establish whether the general results are corroborated between my own human evaluation