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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.
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.
Uyghur Stemming Using Conditional Random Fields
Abdurahim Mahmoud,Akbar Pattar,Askar Hamdulla 보안공학연구지원센터 2015 International Journal of Signal Processing, Image Vol.8 No.8
Stemming is a natural language processing task that to remove all derivational affixes from a word. This task proved to be harder for languages with complex morphology such as the Uyghur language. This paper presents a new stemming method for Uyghur words based on CRFs (Conditional Random Fields). In the proposed method all words in the training corpus are segmented into syllables and each syllable are tagged as a part of stem or as a part of affix. We experimentally evaluated this method with five test files each includes 100 sentences , results have shown that our method gets good performance, average stemming precision, recall and F-score in open test reached 98.42%, 98.34% and 98.38% respectively.