Computer Science ›› 2017, Vol. 44 ›› Issue (10): 228-233.doi: 10.11896/j.issn.1002-137X.2017.10.041

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Anaphoricity Determination of Uyghur Personal Pronouns Based on Deep Belief Network

QIN Yue, YU Long, TIAN Sheng-wei, ZHAO Jian-guo and FENG Guan-jun   

  • Online:2018-12-01 Published:2018-12-01

Abstract: Aiming at the problem that the noise was introduced into the research about anaphoricity determination of personal pronouns in Uyghur language,we represented a method based on deep belief networks(DBN).On the basis of analyzing the grammatical features and linguistic rules of personal pronouns in Uyghur language,we summarized the anaphoricity determination feature set containing ten features.First of all,the Restricted Boltzmann Machine(RBM) network is trained layer by layer in a greedy way,in order to make sure that the feature vector is mapped to the different space so that the characteristic information can be retained as much as possible.Then,the BP network in the last layer is set up and the features of the output vector about RBM are classified,as well as the entire network is trained in a supervised way and it is fine-tuned.The experimental result shows that the accuracy rate of correct recognition of anaphoricity determination about Uyghur personal pronouns reaches 95.17%,which is improved by 9% compared to that of SVM algorithm,and the validation and availability of the method are demonstrated.

Key words: Deep belief networks(DBN),Anaphoricity determination,Uyghur language,Feature extraction

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