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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.
Recognition of Person Name in Uyghur Text Corpus using Naïve Bayes
Abdurahim Mahmoud,Tashpolat Nizamidin,Peride Tursun,Askar Hamdulla 보안공학연구지원센터 2016 International Journal of u- and e- Service, Scienc Vol.9 No.12
This paper presents a novel approach to recognize person name in Uyghur corpus. The Recognition of a person name for Uyghur using Naive Bayes Classifier is a challenging task in intelligent computing. Uyghur person name recognition (UPNR) aims at classifying each word in a document into predefined target label (person name or others) in a linear and non-linear fashion. Some language specific rules are added to recognize person names. Moreover, some gazetteers and context patterns are added to increase its performance as it is observed that identification of rules and context patterns requires language-based knowledge to make the work better. We have used required lexical databases to prepare rules and identify the context patterns for Uyghur. Experimental results show that our approach achieves higher accuracy than previous approaches.