Imperial College London
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Professor Rafael A. Calvo

Faculty of Engineering, Dyson School of Design Engineering

Chair in Engineering Design
 
 
 
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Contact

 

r.calvo

 
 
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Location

 

Dyson BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@inproceedings{Liu:2012:10.1007/978-3-642-30950-2_47,
author = {Liu, M and Calvo, RA},
doi = {10.1007/978-3-642-30950-2_47},
pages = {358--367},
title = {Using information extraction to generate trigger questions for academic writing support},
url = {http://dx.doi.org/10.1007/978-3-642-30950-2_47},
year = {2012}
}
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RIS format (EndNote, RefMan)

TY  - CPAPER
AB  - Automated question generation approaches have been proposed to support reading comprehension. However, these approaches are not suitable for supporting writing activities. We present a novel approach to generate different forms of trigger questions (directive and facilitative) aimed at supporting deep learning. Useful semantic information from Wikipedia articles is extracted and linked to the key phrases in a students' literature review, particularly focusing on extracting information containing 3 types of relations (Kind of, Similar-to and Different-to) by using syntactic pattern matching rules. We collected literature reviews from 23 Engineering research students, and evaluated the quality of 306 computer generated questions and 115 generic questions. Facilitative questions are more useful when it comes to deep learning about the topic, while directive questions are clearer and useful for improving the composition. © 2012 Springer-Verlag.
AU  - Liu,M
AU  - Calvo,RA
DO  - 10.1007/978-3-642-30950-2_47
EP  - 367
PY  - 2012///
SN  - 0302-9743
SP  - 358
TI  - Using information extraction to generate trigger questions for academic writing support
UR  - http://dx.doi.org/10.1007/978-3-642-30950-2_47
ER  -
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