Abstract
In this paper, we discuss tree kernels that can be applied for the classification of XML documents based on their DOM trees. DOM trees are ordered trees, in which every node might be labeled by a vector of attributes including its XML tag and the textual content. We describe four new kernels suitable for this kind of trees: a tree kernel derived from the well-known parse tree kernel, the set tree kernel that allows permutations of children, the string tree kernel being an extension of the so-called partial tree kernel, and the soft tree kernel, which is based on the set tree kernel and takes into a account a “fuzzy” comparison of child positions. We present first results on an artificial data set, a corpus of newspaper articles, for which we want to determine the type (genre) of an article based on its structure alone, and the well-known SUSANNE corpus.
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Geibel, P., Gust, H., Kühnberger, KU. (2007). Variants of Tree Kernels for XML Documents. In: Gelbukh, A., Kuri Morales, Á.F. (eds) MICAI 2007: Advances in Artificial Intelligence. MICAI 2007. Lecture Notes in Computer Science(), vol 4827. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-76631-5_81
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DOI: https://doi.org/10.1007/978-3-540-76631-5_81
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