基于Highway-BiLSTM网络的汉语谓语中心词识别研究

Aiming at the problem of difficult recognition and uniqueness of Chinese predicate head, a Highway-BiLSTM model was proposed. Firstly, multi-layer BiLSTM networks were used to capture multi-granular semantic dependence in a sentence. Then, a Highway network was adopted to alleviate the problem of gradient disappearance. Finally, the output path was optimized by a constraint layer which was designed to guarantee the uniqueness of predicate head. The experimental results show that the proposed method effectively improves the performance of predicate head recognition.