Using information extraction to generate trigger questions for academic writing support

Authors:
Ming Liu;Rafael A. Calvo
Affiliations:
University of Sydney, Sydney, NSW, Australia;University of Sydney, Sydney, NSW, Australia
Venue:
ITS'12 Proceedings of the 11th international conference on Intelligent Tutoring Systems
Year:
2012

Citing 7
Cited 0

Learning Information Extraction Rules for Semi-Structured and Free Text

Machine Learning - Special issue on natural language learning
Snowball: extracting relations from large plain-text collections

DL '00 Proceedings of the fifth ACM conference on Digital libraries
Automatic question generation from text - an aid to independent study

SIGCSE '76 Proceedings of the ACM SIGCSE-SIGCUE technical symposium on Computer science and education
Interpreting comparative constructions in biomedical text

BioNLP '07 Proceedings of the Workshop on BioNLP 2007: Biological, Translational, and Clinical Language Processing
Generating Instruction Automatically for the Reading Strategy of Self-Questioning

Proceedings of the 2009 conference on Artificial Intelligence in Education: Building Learning Systems that Care: From Knowledge Representation to Affective Modelling
Question taxonomy and implications for automatic question generation

AIED'11 Proceedings of the 15th international conference on Artificial intelligence in education
Using Wikipedia and Conceptual Graph Structures to Generate Questions for Academic Writing Support

IEEE Transactions on Learning Technologies

Quantified Score

Hi-index	0.00

Visualization

Abstract

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.