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- W2349245853 abstract "According to the agglutinative feature of Uygur language,and the statistical analysis results of inflected word form and frequency,to resolve the form-type words disambiguation and the data sparsity problem caused by inflection,this paper presents a maximum entropy based on part-of-speech tagging model which integrated the morphological features such as stem,suffix etc. On the basis of this generic POS tagging model,according to the agglutinative nature,the feature template and feature function are designed in this model. The experimental results show that maximum entropy model can be more fitful for Uyghur POS tagging task,and the suffix feature significantly improves form-type word and unknown word tagging accuracy,comparing with word from feature based tagging model,the accuracy has been improved 2. 64%,the precision reaches96. 85%." @default.
- W2349245853 created "2016-06-24" @default.
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- W2349245853 date "2015-01-01" @default.
- W2349245853 modified "2023-09-24" @default.
- W2349245853 title "Fusion of morphological features for Uyghur part-of-speech tagging based on maximum entropy model" @default.
- W2349245853 hasPublicationYear "2015" @default.
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