Volume 9, Issue 2 August 2013, pp. 115–130 Download PDF

Regular Articles

A corpus-based system of error detection and revision suggestion for Spanish learners in Taiwan: A case study

Hui-Chuan Lu1, Yu-Hsin Chu2, & Cheng-Yu Chang3

1 National Cheng Kung University, TAIWAN huichuanlu1@gmail.com
2 National Cheng Kung University, TAIWAN katy0806@hotmail.com
3 National Cheng Kung University, TAIWAN ccy0927@gmail.com

DOI: https://doi.org/10.29140/jaltcall.v9n2.151


Abstract

Compared with English learners, Spanish learners have fewer resources for automatic error detection and revision and following the current integrative Computer Assisted Language Learning (CALL), we combined corpus-based approach and CALL to create the System of Error Detection and Revision Suggestion (SEDRS) for learning Spanish. Through corpus-based data training and related applications, this system was designed specially for learners of Spanish in Taiwan. The Corpus of Taiwanese Learners of Spanish (CTLS) was used as a database to facilitate the development of the system. The learners’ corpus was tagged with part-of-speech (POS) and lemma information, and it was also annotated by native Spanish speakers with revisions corresponding to errors made by students in their original texts. The system can, in real-time, identify tri-gram errors based on training data extracted from the revised texts of the learners’ corpus and provide revision suggestions listed according to usage frequency for users. The system was evaluated by 25 Spanish learners and eight experienced programmers to quantify the system’s practical effectiveness. In addition, feedback from learners' was collected to improve the system in the future.



Copyright

© Hui-Chuan Lu, Yu-Hsin Chu, Cheng-Yu Chang

CC  4.0
This work is licensed under a Creative Commons Attribution 4.0 International License.


Suggested citation

Lu, H.-C., Chu, Y.-H., & Chang, C.-Y. (2013). A corpus-based system of error detection and revision suggestion for Spanish learners in Taiwan: A case study. The JALT CALL Journal, 9(2), 115–130. https://doi.org/10.29140/jaltcall.v9n2.151


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