Using information extraction to generate trigger questions for academic writing support
|Using information extraction to generate trigger questions for academic writing support|
|Author(s)||Liu M., Calvo R.A.|
|Published in||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Keyword(s)||Academic Writing Support, Information Extraction, Question Generation (Extra: Deep learning, Extracting information, Information Extraction, Key-phrase, Literature reviews, Matching rules, Question Generation, Reading comprehension, Semantic information, Syntactic patterns, Wikipedia, Writing activities, Computer aided instruction, Information analysis, Pattern matching, Education)|
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Using information extraction to generate trigger questions for academic writing support is a 2012 conference paper written in English by Liu M., Calvo R.A. and published in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics).
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.
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