Abstract
In this paper, we propose a machine learning method to knowledge element extraction from learning resources. First, we build a knowledge element taxonomy containing 25 semantic types. Second, we formalize the knowledge element extraction of single semantic type as binary classification. Finally, we construct the multi-class classification model which can predict the semantic type of knowledge element by merge the results of binary classifiers. We annotate three semantic types in corpus and use them as training data, train the machine learning models. In experiment, we compared three binary classification models: Decision Tree, SVM and Naïve Bayesian. The experimental results show that SVM has better average performance. We employ ECOC method to construct multi-class classification model and use SVM as base binary classifier in the model. Our approach outperforms the baseline in experiment. The experimental results indicate that our approach is effective.
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Chang, X., Zheng, Q. (2008). Knowledge Element Extraction for Knowledge-Based Learning Resources Organization. In: Leung, H., Li, F., Lau, R., Li, Q. (eds) Advances in Web Based Learning – ICWL 2007. ICWL 2007. Lecture Notes in Computer Science, vol 4823. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78139-4_10
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DOI: https://doi.org/10.1007/978-3-540-78139-4_10
Publisher Name: Springer, Berlin, Heidelberg
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