The Influence of Spelling Errors on Content Scoring Performance

Andrea Horbach, Yuning Ding, Torsten Zesch


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
Venues:
NLP-TEA | WS
SIG:
Publisher:
Asian Federation of Natural Language Processing
Note:
Pages:
45–53
Language:
URL:
https://www.aclweb.org/anthology/W17-5908
DOI:
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PDF:
http://aclanthology.lst.uni-saarland.de/W17-5908.pdf