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Get Semantic With Me! The Usefulness of Different Feature Types for Short-Answer Grading

Ulrike Padó


Abstract
Automated short-answer grading is key to help close the automation loop for large-scale, computerised testing in education. A wide range of features on different levels of linguistic processing has been proposed so far. We investigate the relative importance of the different types of features across a range of standard corpora (both from a language skill and content assessment context, in English and in German). We find that features on the lexical, text similarity and dependency level often suffice to approximate full-model performance. Features derived from semantic processing particularly benefit the linguistically more varied answers in content assessment corpora.
Anthology ID:
C16-1206
Volume:
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers
Month:
December
Year:
2016
Address:
Osaka, Japan
Editors:
Yuji Matsumoto, Rashmi Prasad
Venue:
COLING
SIG:
Publisher:
The COLING 2016 Organizing Committee
Note:
Pages:
2186–2195
Language:
URL:
https://aclanthology.org/C16-1206
DOI:
Bibkey:
Cite (ACL):
Ulrike Padó. 2016. Get Semantic With Me! The Usefulness of Different Feature Types for Short-Answer Grading. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pages 2186–2195, Osaka, Japan. The COLING 2016 Organizing Committee.
Cite (Informal):
Get Semantic With Me! The Usefulness of Different Feature Types for Short-Answer Grading (Padó, COLING 2016)
Copy Citation:
PDF:
https://aclanthology.org/C16-1206.pdf