Abstract
In this paper, we present a simple and elegant algorithm to extract and visualize various concept relationships present in sections of a textbook. This can be easily extended to develop visualizations of entire chapters or textbooks, thereby opening up opportunities for developing a range of visual applications for e-learning and education in general. Our algorithm creates visualizations by mining relationships between concepts present in a text by applying the idea of transitive closure rather than merely counting co-occurrences of terms. It does not require any thesaurus or ontology of concepts. We applied the algorithm to two textbooks - Theory of Computation and Machine Learning - to extract and visualize concept relationships from their sections. Our findings show that the algorithm is capable of capturing deep-set relationships between concepts which could not have been found by using a term co-occurrence approach.
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Sastry, C.S., Jagaluru, D.S., Mahesh, K. (2018). Visualizing Textbook Concepts: Beyond Word Co-occurrences. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2017. Lecture Notes in Computer Science(), vol 10761. Springer, Cham. https://doi.org/10.1007/978-3-319-77113-7_29
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DOI: https://doi.org/10.1007/978-3-319-77113-7_29
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