Abstract
Spelling errors occur frequently in educational settings, but their influence on automatic scoring is largely unknown. We therefore investigate the influence of spelling errors on content scoring performance using the example of the ASAP corpus. We conduct an annotation study on the nature of spelling errors in the ASAP dataset and utilize these finding in machine learning experiments that measure the influence of spelling errors on automatic content scoring. Our main finding is that scoring methods using both token and character n-gram features are robust against spelling errors up to the error frequency in ASAP.- Anthology ID:
- W17-5908
- Volume:
- Proceedings of the 4th Workshop on Natural Language Processing Techniques for Educational Applications (NLPTEA 2017)
- Month:
- December
- Year:
- 2017
- Address:
- Taipei, Taiwan
- Editors:
- Yuen-Hsien Tseng, Hsin-Hsi Chen, Lung-Hao Lee, Liang-Chih Yu
- Venue:
- NLP-TEA
- SIG:
- Publisher:
- Asian Federation of Natural Language Processing
- Note:
- Pages:
- 45–53
- Language:
- URL:
- https://aclanthology.org/W17-5908
- DOI:
- Cite (ACL):
- Andrea Horbach, Yuning Ding, and Torsten Zesch. 2017. The Influence of Spelling Errors on Content Scoring Performance. In Proceedings of the 4th Workshop on Natural Language Processing Techniques for Educational Applications (NLPTEA 2017), pages 45–53, Taipei, Taiwan. Asian Federation of Natural Language Processing.
- Cite (Informal):
- The Influence of Spelling Errors on Content Scoring Performance (Horbach et al., NLP-TEA 2017)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/W17-5908.pdf