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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References
Benjamin, R.G.: Reconstructing readability: Recent developments and recommendations in the analysis of text difficulty. Educational Psychology Review 24(1), 63–88 (2012)
Blei, D.M.: Probabilistic topic models. Communications of the ACM 55(4), 77–84 (2012)
Collins-Thompson, K., Bennett, P.N., White, R.W., de la Chica, S., Sontag, D.: Personalizing web search results by reading level. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management, pp. 403–412. ACM (2011)
Collins-Thompson, K., Callan, J.P.: A language modeling approach to predicting reading difficulty. In: Proceedings of the Human Language Technology Conference of the North American Chapter of the Association for Computational Linguistics, pp. 193–200 (2004)
Daitch, S.I., Kelner, J.A., Spielman, D.A.: Fitting a graph to vector data. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 201–208. ACM (2009)
Feng, L., Jansche, M., Huenerfauth, M., Elhadad, N.: A comparison of features for automatic readability assessment. In: Proceedings of the 23rd International Conference on Computational Linguistics: Posters, pp. 276–284 (2010)
François, T., Fairon, C.: An ai readability formula for french as a foreign language. In: Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, pp. 466–477 (2012)
Jameel, S., Qian, X., Lam, W.: N-gram fragment sequence based unsupervised domain-specific document readability. In: COLING, pp. 1309–1326 (2012)
Jebara, T., Wang, J., Chang, S.F.: Graph construction and b-matching for semi-supervised learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 441–448. ACM (2009)
Jiang, Z., Sun, G., Gu, Q., Chen, D.: An ordinal multi-class classification method for readability assessment of chinese documents. In: Buchmann, R., Kifor, C.V., Yu, J. (eds.) KSEM 2014. LNCS, vol. 8793, pp. 61–72. Springer, Heidelberg (2014)
Kim, D.S., Verma, K., Yeh, P.Z.: Joint extraction and labeling via graph propagation for dictionary construction. In: Twenty-Seventh AAAI Conference on Artificial Intelligence (2013)
Kincaid, J.P., Fishburne Jr., R.P., Rogers, R.L., Chissom, B.S.: Derivation of new readability formulas (automated readability index, fog count and flesch reading ease formula) for navy enlisted personnel, Tech. rep., DTIC Document (1975)
Ma, Y., Fosler-Lussier, E., Lofthus, R.: Ranking-based readability assessment for early primary children’s literature. In: Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 548–552 (2012)
Salton, G., Buckley, C.: Term-weighting approaches in automatic text retrieval. Information Processing & Management 24(5), 513–523 (1988)
Subramanya, A., Petrov, S., Pereira, F.: Efficient graph-based semi-supervised learning of structured tagging models. In: Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing, pp. 167–176 (2010)
Tanaka-Ishii, K., Tezuka, S., Terada, H.: Sorting texts by readability. Computational Linguistics 36(2), 203–227 (2010)
Vogel, M., Washburne, C.: An objective method of determining grade placement of children’s reading material. The Elementary School Journal 28(5), 373–381 (1928)
Xie, W., Peng, Y., Xiao, J.: Semantic graph construction for weakly-supervised image parsing. In: Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence, Québec City, Québec, Canada, July 27-31, pp. 2853–2859 (2014)
Zeng, X., Wong, D.F., Chao, L.S., Trancoso, I.: Graph-based semi-supervised model for joint chinese word segmentation and part-of-speech tagging. In: Proceeding of the 51st Annual Meeting of the Association for Computational Linguistics, pp. 770–779 (2013)
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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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