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RISS 인기검색어
- 검색키워드
- 저자 : Palidan Tuerxun (검색결과 3 건)
- 무료
- 기관 내 무료
- 유료
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The SVM based Uyghur Text Classification and its Performance Analysis
Palidan Tuerxun,Fang Dingyi,Askar Hamdulla 보안공학연구지원센터 2015 International Journal of Multimedia and Ubiquitous Vol.10 No.4
This paper mainly explores the use of Support Vector Machines (SVMs) for Uyghur text classification, presents the process of text categorization: Text preprocessing, feature dimensionality reduction, representation method and classification of text features etc., discusses the SVMs classification algorithm in the application of Uyghur text classification. Focus on the construction of text categorization model and its procedures. Experiment results show that training by using the selected training data with the guarantee of the performance of the classifier, has higher efficiency than other nearest neighbor classifier (KNN), Naive Bayes (NB) classifier with increased accuracy.
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A Survey of Uyghur Person Name Recognition
Tashpolat Nizamidin,Palidan Tuerxun,Askar Hamdulla,Muhtar Arkin 보안공학연구지원센터 2016 International Journal of Signal Processing, Image Vol.9 No.3
Uyghur is one of the most populous and civilized groups with Turkic ethnicity and mainly located Xinjiang Uyghur Autonomous Region of China. Uyghur language belongs to the Karluk branch of the Turkic language family in Altaic language system, and holds agglutinative characteristics in morphological structure. Named Entity Recognition (NER) is an Information Extraction task that has become an essential part of Natural Language Processing (NLP) tasks, such as Machine Translation and Information Retrieval. In this paper, as a subtask of NER, the importance of Uyghur Named Entity Recognition (UPNR) task is demonstrated, the main characteristics of the Uyghur language are highlighted, and the aspects of standardization in annotating named entities are illustrated. Moreover, the approaches used in Uyghur NPNR field are explained and the features of common tools used in Uyghur NPNR are described. A brief review of the state of the art of Uyghur NPNR research is discussed, too. Finally, we present our conclusions. Throughout the presentation, illustrative examples are used for clarification.
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Uyghur Stemming and Lemmatization Approach based on Multi-Morphological Features
Abdurahim Mahmoud,Sediyegvl Enwer,Abdusalam Dawut,Palidan Tuerxun,Askar Hamdulla 보안공학연구지원센터 2016 International Journal of Database Theory and Appli Vol.9 No.11
This paper describes a stemming and lemmatization approach for Uyghur using Conditional Random Fields (CRFs). In the proposed approach, we used syllable-level training and test corpus with the combination of some automatically tagged positional and morphological feature tags. The training and test corpus has been manually tagged with a stemming tag set which includes eight kinds of tags which fully reflect the morphological feature of Uyghur word. It has been observed that some morphological features are very helpful for improving the evaluating results. The syllable-level Precision, Recall and F-score of the best evaluation result respectively are 98.79%,98.71% and 98.75% respectively, and the word-level accuracy we achieved is 95.9%.The experimental results show that the efficiency of this approach is very ideal.
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