Advertisement

Corpus-Based Correlational Study of Terms and Quality in Business English Writing

  • Shili Ge
  • Jingchao Zhang
  • Xiaoxiao ChenEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10108)

Abstract

One of the most important tasks in automated essay scoring (AES) is feature selection. Terms are indispensable in Business English (BE) writing. In order to analyze the possibility of involving terms in BE writing automated scoring feature set, the strength of correlations between terminological features and writing quality or scores is studied. A Business English term bank (BETB) was built based on a term dictionary. With BETB and a self-coded Python program, business terms and their categories in a BE writing corpus were identified and extracted. The analysis shows that, among ten categories of terms and total term numbers in BE writing, human resource terms and total term numbers have a moderate correlation with writing scores. This result means business terms, especially writing content related terms, should be covered in business AES feature set, which can improve the performance of AES systems and facilitate BE learners’ writing proficiency.

Keywords

Automated essay scoring Business English writing Terminology Feature selection 

Notes

Acknowledgements

This work is financially supported by the National Social Science Fund (No. 13BYY097).

References

  1. 1.
    Attali, Y., Burstein, J.: Automated essay scoring with e-rater® V.2. J. Technol. Learn. Assess. 4, 3–30 (2006)Google Scholar
  2. 2.
    Ellis, M., Johnson, C.: Teaching Business English. Oxford University Press, Oxford (2014)Google Scholar
  3. 3.
    Valenti, S., Neri, F., Cucchiarelli, A.: An overview of current research on automated essay grading. J. Inf. Technol. Educ. 2, 319–330 (2003)Google Scholar
  4. 4.
    Huang, Z., Xie, J., Xun, E.: Study of feature selection in HSK automated essay scoring. Comput. Eng. Appl. 50, 118–122 (2014)Google Scholar
  5. 5.
    Ge, S.: A Research on General Computerized Composition Scoring and Feed-Back for College English Teaching in China. Shanghai Foreign Language Education Press, Shanghai (2015)Google Scholar
  6. 6.
    Feng, L.: The Writing of Business. Jiling Publishing Group Co. Ltd., Changchun (2010)Google Scholar
  7. 7.
    Zhang, Z.: Business English students learning to write for international business: what do international business practitioners have to say about their texts? Engl. Specif. Purp. 32, 144–156 (2013)CrossRefGoogle Scholar
  8. 8.
    Bourigault, D., Jacquemin, C., L’Homme, M.: Introduction of Recent Advances in Computational Terminology. John Benjamins Publishing Company, Amsterdam/Philadelphia (2001)CrossRefGoogle Scholar
  9. 9.
    Wright, S.E.: Term selection: the initial phase of terminology management. In: Wright, S.E., Budin, G. (eds.) Handbook of Terminology Management, vol. 1, Basic Aspects of Terminology Management, pp. 13–23. John Benjamins Publishing Company, Amsterdam/Philadelphia (1997)Google Scholar
  10. 10.
    Cabré, M.T.: Theories of terminology: their description, prescription and explanation. Terminology 9, 163–199 (2003)CrossRefGoogle Scholar
  11. 11.
    Temmerman, R.: Towards New Ways of Terminology Description: The Sociocognitive-Approach, vol. 3. John Benjamins Publishing, Philadelphia (2000)CrossRefGoogle Scholar
  12. 12.
    Ahmad, K., Rogers, M.: Corpus-related applications. In: Wright, S.E., Budin, G (eds.) Handbook of Terminology Management, vol. 2, Application-Oriented Terminology Management, pp. 725–760. John Benjamins Publishing Company, Amsterdam/Philadelphia (2001)Google Scholar
  13. 13.
    Picht, H., Draskau, J.: Terminology: An Introduction. University of Surrey, Guildford (1985)Google Scholar
  14. 14.
    Drouin, P.: Une Methodologie d’identification automatique des syntagmes terminologiques: l’apport de la description du non-terme. META 42, 45–54 (1997)CrossRefGoogle Scholar
  15. 15.
    Pazienza, M.T., Pennacchiotti, M., Zanzotto, F.M.: Terminology extraction: an analysis of linguistics and statistical approaches. In: Sirmakessis, S. (ed.) Knowledge Mining: Proceedings of the NEMIS 2004 Final Conference, vol. 185, pp. 255–279. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  16. 16.
    Ananiadou, S., Sullivan, D., Black, W., Levow, G.A., Gillespie, J., Mao, C., Pyysalo, S., Kollu-ru, B., Tsujii, J., Sorbarai, B.: Systematic association of genes to phenotypes by genome and literature mining. PLoS ONE 6, e14780 (2005)CrossRefGoogle Scholar
  17. 17.
    Bunescu, R., Ge, R., Kate, R., Marcotte, E., Mooney, R., Ramani, A., Wong, Y.: Comparative experiments on learning information extractors for proteins and their interactions. Artif. Intell. Med. 33, 139–155 (2005)CrossRefGoogle Scholar
  18. 18.
    Selinker, L.: Interlanguage. Int. Rev. Appl. Linguist. 10, 209–241 (1972)Google Scholar
  19. 19.
    Wang, J.M., Fang, X.J.: An English-Chinese Dictionary of Business Management. Foreign Language Teaching and Research Press, Beijing (2010)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.School of English for International BusinessGuangdong University of Foreign StudiesGuangzhouChina
  2. 2.Guangdong Collaborative Innovation Center for Language Research and ServiceGuangdong University of Foreign StudiesGuangzhouChina

Personalised recommendations