01966nas a2200217   4500008004100000245005200041210005200093260000900145300001400154490000700168520130000175653001901475653002101494653002701515653002401542653002801566100001401594700001101608700001201619856011701631       2012                        eng d00aWord combination kernel for text categorization0 aWord combination kernel for text categorization  c2012  a202 - 2110 v103 aWe proposed a novel kernel for text categorization. This kernel is an inner product in the feature space generated by all word combinations of specified length. A word combination is a collection of different words co-occurring in the same sentence. The word combination of length k is weighted by the k-th root of the product of the inverse document frequencies (IDF) of its words. A computationally simple and efficient algorithm was proposed to calculate this kernel. By restricting the words of a word combination to the same sentence and considering multi-word combinations, the word combination features can capture similarity at a more specific level than single words. By discarding word order, the word combination features are more compatible with the flexibility of natural language and the dimensionality this kernel can be reduced significantly compared to the word-sequence kernel. We conducted a series of experiments on the Reuters-21578 dataset and 20 Newsgroups dataset. This kernel consistently achieves better performance than the classical word kernel and word-sequence kernel on the two datasets. We also assessed the impact of word combination length on performance and compared the computing efficiency of this kernel to those of the word kernel and word-sequence kernel.10aKernel methods10aMachine learning10aSupport vector machine10aText classification10aWord-combination kernel1 aZhang, L.1 aHu, X.1 aQin, S.  uhttp://www.scopus.com/inward/record.url?eid=2-s2.0-84866439932&partnerID=40&md5=8649dfc8c1ee23ca70f2b137a74a19e4