A classifier for semi-structured documents
A classifier for semi-structured documents
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DOI：
10.1145/347090.347164
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发表时间：
2000-08
期刊：
影响因子：
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通讯作者：
Jeonghee Yi;Neel Sundaresan
中科院分区：
文献类型：
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作者：
Jeonghee Yi;Neel Sundaresan
In this paper, w edescribe a novel text classi er that can e ectiv ely cope with structured documents. We report experiments that compare its performance with that of a wellknown probabilistic classi er. Our novel classi er can take adv antage of the information in the structure of document that con ventional, purely term-based classi ers ignore.Conventional classi ers are mostly based on the vector space model of document, which views a document simply as an n-dimensional vector of terms. T o retain the information in the structure, w e ha ve dev eloped a structured vector model, which represents a document with a structured vector, whose elements can be either terms or other structured vectors. With this extended model, we also have improved the well-kno wn probabilistic classi cation method based on the Bernoulli document generation model. Our classi er based on these improvements performes signi cantly better on pre-classi ed samples from the web and the US Patent database than the usual classi ers.
