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Using information extraction to generate trigger questions for academic writing support
Abstract Automated question generation approaches hAutomated 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. and useful for improving the composition.
Abstractsub Automated question generation approaches hAutomated 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. and useful for improving the composition.
Bibtextype inproceedings  +
Doi 10.1007/978-3-642-30950-2_47  +
Has author Liu M. + , Calvo R.A. +
Has extra keyword Deep learning + , Extracting information + , Information extraction + , Key-phrase + , Literature review + , Matching rules + , Question Generation + , Reading comprehension + , Semantic information + , Syntactic patterns + , Wikipedia + , Writing activities + , Computer aided instruction + , Information analysis + , Pattern matching + , Education +
Has keyword Academic Writing Support + , Information extraction + , Question Generation +
Isbn 9783642309496  +
Language English +
Number of citations by publication 0  +
Number of references by publication 0  +
Pages 358–367  +
Published in Lecture Notes in Computer Science +
Title Using information extraction to generate trigger questions for academic writing support +
Type conference paper  +
Volume 7315 LNCS  +
Year 2012 +
Creation dateThis property is a special property in this wiki. 8 November 2014 07:16:24  +
Categories Publications without license parameter  + , Publications without remote mirror parameter  + , Publications without archive mirror parameter  + , Publications without paywall mirror parameter  + , Conference papers  + , Publications without references parameter  + , Publications  +
Modification dateThis property is a special property in this wiki. 8 November 2014 07:16:24  +
DateThis property is a special property in this wiki. 2012  +
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