Abstract
Readability assessment is to evaluate the reading difficulty of a document, which can be quantified as reading levels. In this paper, we propose an extended graph-based label propagation method for readability assessment. We employ three vector space models (VSMs) to compute edges and weights for the graphs, along with three graph sparsification techniques. By incorporating the pre-classification results, we develop four strategies to reinforce the graphs before label propagation to capture the ordinal relation among the reading levels. The reinforcement includes recomputing weights for the edges, and filtering out edges linking nodes with big level difference. Experiments are conducted systematically on datasets of both English and Chinese. The results demonstrate both effectiveness and potential of the proposed method.
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Jiang, Z., Sun, G., Gu, Q., Yu, L., Chen, D. (2015). An Extended Graph-Based Label Propagation Method for Readability Assessment. In: Cheng, R., Cui, B., Zhang, Z., Cai, R., Xu, J. (eds) Web Technologies and Applications. APWeb 2015. Lecture Notes in Computer Science(), vol 9313. Springer, Cham. https://doi.org/10.1007/978-3-319-25255-1_40
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DOI: https://doi.org/10.1007/978-3-319-25255-1_40
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