Title :
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
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;
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
DOI :
10.1109/ITSIM.2008.4632066
