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
- Venue:
- COLING
- SIG:
- Publisher:
- The COLING 2016 Organizing Committee
- Note:
- Pages:
- 2186–2195
- Language:
- URL:
- https://aclanthology.org/C16-1206
- DOI:
- 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)
- PDF:
- https://preview.aclanthology.org/nodalida-main-page/C16-1206.pdf