Generic text summarization using relevance measure and latent semantic analysis
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
The Debate on Automated Essay Grading
IEEE Intelligent Systems
Automatic acquisition of hyponyms from large text corpora
COLING '92 Proceedings of the 14th conference on Computational linguistics - Volume 2
Using concept maps in digital libraries as a cross-language resource discovery tool
Proceedings of the 5th ACM/IEEE-CS joint conference on Digital libraries
Mining e-Learning domain concept map from academic articles
Computers & Education
Building domain ontologies from text for educational purposes
EC-TEL'07 Proceedings of the Second European conference on Technology Enhanced Learning: creating new learning experiences on a global scale
Mining concept maps from news stories for measuring civic scientific literacy in media
Computers & Education
Expert Systems with Applications: An International Journal
Single document semantic spaces
AusDM '09 Proceedings of the Eighth Australasian Data Mining Conference - Volume 101
Concept map construction from text documents using affinity propagation
Journal of Information Science
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Concept maps are visual representations of knowledge, widely used in educational contexts. We use the term "Concept Map Mining" (CMM) to refer to the automatic extraction of Concept Maps from documents such as essays. The principles behind CMM have been proposed for applications such as: information extraction in specific knowledge domains, the measurement of student understanding and misconceptions based on written essays, and as a preliminary step to creating domain ontologies.Previous work on the automatic extraction of concept maps present two problems: 1) overly simplistic and varying definitions of concept maps, and 2) the lack of an evaluation framework that can be used to measure the quality of the generated maps. In this paper, we propose a formal definition of the term CMM, with a focus on educational applications.We also propose an evaluation framework that will allow other researchers to share a common ground to evaluate the performance of CMM methods.