@inproceedings{pado-2016-get,
title = "Get Semantic With Me! The Usefulness of Different Feature Types for Short-Answer Grading",
author = "Pad{\'o}, Ulrike",
editor = "Matsumoto, Yuji and
Prasad, Rashmi",
booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
month = dec,
year = "2016",
address = "Osaka, Japan",
publisher = "The COLING 2016 Organizing Committee",
url = "https://aclanthology.org/C16-1206",
pages = "2186--2195",
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.",
}
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%0 Conference Proceedings
%T Get Semantic With Me! The Usefulness of Different Feature Types for Short-Answer Grading
%A Padó, Ulrike
%Y Matsumoto, Yuji
%Y Prasad, Rashmi
%S Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers
%D 2016
%8 December
%I The COLING 2016 Organizing Committee
%C Osaka, Japan
%F pado-2016-get
%X 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.
%U https://aclanthology.org/C16-1206
%P 2186-2195
Markdown (Informal)
[Get Semantic With Me! The Usefulness of Different Feature Types for Short-Answer Grading](https://aclanthology.org/C16-1206) (Padó, COLING 2016)
ACL