A classifier for semi-structured documents
Jeonghee Yi, Neel Sundaresan · 2000
In this paper, w e describe a novel text classi er that can eectiv ely cope with structured documents.We r e p o r t e xperiments 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 ven tional, purely term-based classi ers ignore.Conven tional 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 a l s o h a ve i m p r o ved 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.