1.School of Software, Xinjiang University, Urumqi, Xinjiang 830008, China; 2.Net Center, Xinjiang University, Urumqi, Xinjiang 830046, China; 3.School of Humanities, Xinjiang University, Urumqi, Xinjiang 830046, China; 4. School of Information Science and Engineering, Xinjiang University, Urumqi, Xinjiang 830046, China
Abstract：Focusedon Uyghur noun phrase coreference identification task, this paper proposed a Stacked Nonnegative Constrained Autoencoder( SNCAE) for anaphoricity determination based on semantic feature. Through the analysis of Uyghur noun phrase language phenomenon, 15 kinds of semantic features are extracted, and then input into SNCAE to extract the deep semantic features. Finally, the Softmax classifier is used to complete the recognition task. Compared with Support Vector Machine (SVM), the positive accuracy and negative accurate increased by 8.259% and 4.158%, respectively, and increased by 1.884% and 1.590%, respectively, than the Stacked Autoencoder (SAE).
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2017, Vol. 31 
