Arabic part-of-speech tagger based Support Vectors Machines

Author

Yousif, Jabar Hassan ; Sembok, Tengku Mohd Tengku

Author_Institution

Fac. of Inf. Sci. & Technol., Nat. Univ. of Malaysia, Bangi

Volume

3

fYear

2008

fDate

26-28 Aug. 2008

Firstpage

1

Lastpage

7

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.

Keywords

radial basis function networks; speech processing; support vector machines; text analysis; Arabic part-of-speech tagger; NeuroSolutions software; RBF; SVM; radial basis functions; support vectors machines; training phase; Data mining; Information science; Kernel; Linear approximation; Speech; Support vector machine classification; Support vector machines; Tagging; Text mining; Writing;

fLanguage

English

Publisher

ieee

Conference_Titel

Information Technology, 2008. ITSim 2008. International Symposium on

Conference_Location

Kuala Lumpur

Print_ISBN

978-1-4244-2327-9

Electronic_ISBN

978-1-4244-2328-6

Type

conf

DOI

10.1109/ITSIM.2008.4632066

Filename

4632066