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  2013, Vol. 26 Issue (9): 845-852    DOI:
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Unsupervised Uyghur Segmentation and Unsupervised Feature Selection
TOHTI Turdi,PATTA Akbarr,HAMDULLA Askar
School of Information Science and Engineering,Xinjiang University,Urumqi 830046

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Abstract  Commonly used Uyghur segmentation method produces a large number of semantic abstraction and even polysemous word features,so learning algorithms are difficult to find the hidden structure in the high-dimensional data. A segmentation approach dme-TS and a feature selection approach UMRMR-UFS based on unsupervised strategy are proposed. In dme-TS,the word based Bi-gram and contextual information are derived from large scale raw text corpus automatically,and the liner combinations of difference of t-test,mutual information and entropy of double word adjacency are taken as a measurement (dme) to estimate the agglutinative strength between two adjacent Uyghur words. In UMRMR-UFS,an improved unsupervised feature selection criterion (UMRMR) is proposed and the importance of each feature is estimated according to its minimum redundancy and maximum relevancy. The experimental result shows that dme-TS effectively reduces the dimensions of original feature set and improves the quality of the feature itself,and the learning algorithm represents its highest performance on the feature subset selected by UMRMR-UFS.
Key wordsUyghur Segmentation      Mutual Information      Difference of t-Test      Entropy of Adjacency      Unsupervised Feature Selection     
Received: 14 August 2012     
ZTFLH: TP391  
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TOHTI Turdi
PATTA Akbarr
HAMDULLA Askar
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TOHTI Turdi,PATTA Akbarr,HAMDULLA Askar. Unsupervised Uyghur Segmentation and Unsupervised Feature Selection[J]. , 2013, 26(9): 845-852.
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http://manu46.magtech.com.cn/Jweb_prai/EN/      OR     http://manu46.magtech.com.cn/Jweb_prai/EN/Y2013/V26/I9/845
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