Advertisement

Automatic Correction of Definite Article Redundancy Error in the English Compositions of College Students

  • Lei Wang
  • Ting Wang
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 843)

Abstract

Scholars have conducted research on the errors made by language learners in writing both theoretically and practically, and they have made considerable breakthroughs in Second Language Acquisition (SLA) with respect of writing. The learners’ use of articles in English writing displays a certain pattern with factors such as influence from a learner’s mother language, personal idiosyncracy or common misunderstanding. Thus it is feasible to judge whether an article is used correctly in a certain context on a rule basis. This paper adopts a rule-based method, which lets the computer learn how to use articles from empirical sources and automatic correct the Definite Article Redundancy Errors (DAREs) committed by students in their English compositions. With the help of both grammatical and contextual information, the rules in our system are deduced from authentic examples and possess authority and accuracy. The system also allows teachers to add or alter these rules flexibly by either examining the actual cases or updating their professional knowledge as they wish. By doing this, we hope the quality and efficiency of marking compositions in Chinese colleges will be improved.

Keywords

Automatic correction Definite Article Redundancy Error College English writing 

Notes

Acknowledgment

This work is supported by the grant “Research on Teaching College English Writing and Modern Educational Technology (2013)” from Beijing College English Research Association.

References

  1. 1.
    Ellis, R.: Understanding Second Language Acquisition. Oxford University Press, Oxford (1985)Google Scholar
  2. 2.
    Pica, T.: The article in American English: what the textbooks don’t tell us? In: Wolfson, N., Judd, E. (eds.) Sociolinguistics and Language Acquisition, pp. 222–233. Newbury House, Rowley (1983)Google Scholar
  3. 3.
    Master, P.: Consciousness raising and article pedagogy. In: Belcher, D., Braine, G. (eds.) Academic Writing in Second Language Essays on Research and Pedagogy, pp. 183–204. Ablex, Norwood (1995)Google Scholar
  4. 4.
    Lee, K., et al.: Referential place-holding in Chinese children’s acquisition of English articles. Appl. Psycholinguist. 15, 29–43 (1994)CrossRefGoogle Scholar
  5. 5.
    Robertson, D.: Variability in the use of the English article system by Chinese learners of English. Second Lang. Res. 16, 135–172 (2000)CrossRefGoogle Scholar
  6. 6.
    Wang, J.: Cohesion and coherence of the definite article at level discourse/text. J. Sichuan Int. Stud. Univ. 17(6), 68–71 (2001)Google Scholar
  7. 7.
    Li, J., Cai, J.: The article errors in Chinese students’ writing based on corpus. J. PLA Foreign Lang. Coll. 6, 8–62 (2001)Google Scholar
  8. 8.
    Wang, J.: The acquisition of non-generic use of definite articles of Chinese learners. J. Foreign Lang. Educ. 26(3), 22–26 (2005)Google Scholar
  9. 9.
    Knight, K., et al.: Automated postediting of documents. In: Proceedings of AAAI 1994 (1994)Google Scholar
  10. 10.
    Chang, B., Liu, Y., Liu, Q.: Research on article selection in Chinese-English machine translation. J. Chin. Inf. Process. 12(2), 8–14 (1998)Google Scholar
  11. 11.
    Ning, W., Cai, D., Zhang, G., Ji, D., Miao, X.: Research on article choice based on conditional random fields. J. Chin. Inf. Process. 22(6), 20 (2008)Google Scholar
  12. 12.
    Department of Higher Education, Ministry of Education of China: College English Curriculum Requirements. Shanghai Foreign Language Education Press (2007)Google Scholar
  13. 13.
    Corder, S.P.: Error Analysis and Interlanguage. Oxford University Press, Oxford (1976)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.School of Foreign LanguagesPeking UniversityBeijingChina
  2. 2.No. 1 Senior High School of LiaoyangLiaoyangChina

Personalised recommendations