Abstract
We propose a Bayesian probabilistic modeling of the semantic structures of HTML documents. We assume that HTML documents have logically hierarchical structures and model them as links between blocks. These links or dependency structures are estimated by sampling methods. We use hierarchical Bayesian modeling where each block is given labels such as “heading” or “contents”, and words and layout features (i.e., symbols and HTML tags) are generated simultaneously, based on these labels.
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Yoshida, M., Matsumoto, K., Kita, K., Nakagawa, H. (2014). Unsupervised Analysis of Web Page Semantic Structures by Hierarchical Bayesian Modeling. In: Tseng, V.S., Ho, T.B., Zhou, ZH., Chen, A.L.P., Kao, HY. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2014. Lecture Notes in Computer Science(), vol 8444. Springer, Cham. https://doi.org/10.1007/978-3-319-06605-9_47
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DOI: https://doi.org/10.1007/978-3-319-06605-9_47
Publisher Name: Springer, Cham
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