Specific to the anaphora phenomenon of Uyghur personal pronouns

a deep learning mechanism of Bi-LSTM (Bi-directional long short term memory) network is proposed

which is based on the deep semantic information to resolve anaphora resolution problem in Uyghur personal pronouns.Firstly

make the word embedding which contain semantic and syntactic information as the input of Bi-LSTM

to excavate the implicit semantic features of Uyghur.Secondly

explore the anaphora phenomenon in Uyghur and extract 24 hand-crafted features.Then

use multilayer perception(MLP)to concatenate hand-crafted features and context semantic features.Finally

two types of features are used to train the softmax classifier to complete the task.The experimental results show that

on the basis of full utilization of the advantages of two types of features

the F1 value of anaphora resolution is 76.86%.It is proved that Bi-LSTM is more capable of mining implicit context semantic information than LSTM、SVM as well as ANN

and the introduction of hand-crafted features can effectively improve the performance.