Arabic part-of-speech tagger based support vectors machines

被引：0

作者：

Yousif, Jabar Hassan ^{[1
]}

Sembok, Tengku Mohd Tengku ^{[2
]}

机构：

[1] Univ Kebangsaan Malaysia, Fac Informat Sci & Technol, Ukm Bangi 43600, Selangor, Malaysia

[2] Natl Def Univ Malaysia, Kuala Lumpur 57000, Malaysia

来源：

INTERNATIONAL SYMPOSIUM OF INFORMATION TECHNOLOGY 2008, VOLS 1-4, PROCEEDINGS: COGNITIVE INFORMATICS: BRIDGING NATURAL AND ARTIFICIAL KNOWLEDGE | 2008年

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Support Vector Machines (SVMs) and related kernal 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.

引用

页码：2084 / +

页数：3

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