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)


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.


Automated essay scoring Business English writing Terminology Feature selection 



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


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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

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