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- W2162527272 abstract "Support vector machines (SVMs) and related kernel methods have become widely known tools for text mining tasks such as classification and regression. The Arabic part of speech (POS) based support vectors machine is designed and implemented. The NeuroSolutions software is used to adopt and learn the proposed tagger. The radial basis functions (RBFs) is used as a linear function approximator. The experiments has give an evinced that the SVMs tagger is accurate of (99.99%), has low processing time, and use a little a mount of data at training phase." @default.
- W2162527272 created "2016-06-24" @default.
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- W2162527272 date "2008-08-01" @default.
- W2162527272 modified "2023-09-23" @default.
- W2162527272 title "Arabic part-of-speech tagger based Support Vectors Machines" @default.
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- W2162527272 doi "https://doi.org/10.1109/itsim.2008.4632066" @default.
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