Bidirectional Long Short-Term Memory Network with a Conditional Random Field Layer for Uyghur Part-Of-Speech Tagging
AbstractUyghur is an agglutinative and a morphologically rich language; natural language processing tasks in Uyghur can be a challenge. Word morphology is important in Uyghur part-of-speech (POS) tagging. However, POS tagging performance suffers from error propagation of morphological analyzers. To address this problem, we propose a few models for POS tagging: conditional random fields (CRF), long short-term memory (LSTM), bidirectional LSTM networks (BI-LSTM), LSTM networks with a CRF layer, and BI-LSTM networks with a CRF layer. These models do not depend on stemming and word disambiguation for Uyghur and combine hand-crafted features with neural network models. State-of-the-art performance on Uyghur POS tagging is achieved on test data sets using the proposed approach: 98.41% accuracy on 15 labels and 95.74% accuracy on 64 labels, which are 2.71% and 4% improvements, respectively, over the CRF model results. Using engineered features, our model achieves further improvements of 0.2% (15 labels) and 0.48% (64 labels). The results indicate that the proposed method could be an effective approach for POS tagging in other morphologically rich languages. View Full-Text
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Maimaiti, M.; Wumaier, A.; Abiderexiti, K.; Yibulayin, T. Bidirectional Long Short-Term Memory Network with a Conditional Random Field Layer for Uyghur Part-Of-Speech Tagging. Information 2017, 8, 157.
Maimaiti M, Wumaier A, Abiderexiti K, Yibulayin T. Bidirectional Long Short-Term Memory Network with a Conditional Random Field Layer for Uyghur Part-Of-Speech Tagging. Information. 2017; 8(4):157.Chicago/Turabian Style
Maimaiti, Maihemuti; Wumaier, Aishan; Abiderexiti, Kahaerjiang; Yibulayin, Tuergen. 2017. "Bidirectional Long Short-Term Memory Network with a Conditional Random Field Layer for Uyghur Part-Of-Speech Tagging." Information 8, no. 4: 157.
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