Abstract
We investigate automatically extracting multiword topical components to replace information currently provided by experts that is used to score the Evidence dimension of a writing in response to text assessment. Our goal is to reduce the amount of expert effort and improve the scalability of an automatic scoring system. Experimental results show that scoring performance using automatically extracted data-driven topical components is promising.
Cite
CITATION STYLE
Rahimi, Z., & Litman, D. (2016). Automatically extracting topical components for a response-to-text writing assessment. In Proceedings of the 11th Workshop on Innovative Use of NLP for Building Educational Applications, BEA 2016 at the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2016 (pp. 277–282). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/w16-0532
Register to see more suggestions
Mendeley helps you to discover research relevant for your work.
