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	Chinese Semantic Role Labeling with bidirectional recurrent neural networks
	Wang, Zhen ; Jiang, Tingsong ; Chang, Baobao ; Sui, Zhifang
	2015
英文摘要	Traditional approaches to Chinese Semantic Role Labeling (SRL) almost heavily rely on feature engineering. Even worse, the long-range dependencies in a sentence can hardly be modeled by these methods. In this paper, we introduce bidirectional recurrent neural network (RNN) with long-short-term memory (LSTM) to capture bidirectional and long-range dependencies in a sentence with minimal feature engineering. Experimental results on Chinese Proposition Bank (CPB) show a significant improvement over the state-of the-art methods. Moreover, our model makes it convenient to introduce heterogeneous resource, which makes a further improvement on our experimental performance. ? 2015 Association for Computational Linguistics.; EI; 1626-1631
语种	英语
出处	Conference on Empirical Methods in Natural Language Processing, EMNLP 2015
内容类型	其他
源URL	[http://ir.pku.edu.cn/handle/20.500.11897/436963]
专题	信息科学技术学院
推荐引用方式 GB/T 7714	Wang, Zhen,Jiang, Tingsong,Chang, Baobao,et al. Chinese Semantic Role Labeling with bidirectional recurrent neural networks. 2015-01-01.

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