Chinese Semantic Role Labeling with Bidirectional Recurrent Neural Networks

Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pages 1626–1631, Lisbon, Portugal, 17-21 September 2015. c⃝2015 Association for Computational Linguistics. Chinese Semantic Role Labeling with Bidirectional Recurrent Neural Networks Zhen Wang, Tingsong Jiang, Baobao Chang, Zhifang Sui Key Laboratory of Computational Linguistics, Ministry of Education School of Electronics Engineering and Computer Science, Peking University Collaborative Innovation Center for Language Ability, Xuzhou 221009 China wzpkuer@gmail.com, {tingsong, chbb, szf}@pku.edu.cn Abstract Traditional approaches to Chinese Seman- tic Role Labeling (SRL) almost heavily re- ly on feature engineering. Even worse, the long-range dependencies in a sentence can hardly be modeled by these method- s. In this paper, we introduce bidirection- al recurrent neural network (RNN) with long-short-term memory (LSTM) to cap- ture bidirectional and long-range depen- dencies in a sentence with minimal fea- ture 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 hetero- geneous resource, which makes a further improvement on our experimental perfor- mance. 1 Introduction Semantic Role Labeling (SRL) is defined as the task to recognize arguments for a given predicate and assign semantic role labels to them. Because of its ability to encode semantic information, there has been an increasing interest in SRL on many languages (Gildea and Jurafsky, 2002; Sun and Ju- rafsky, 2004). Figure 1 shows an example in Chi- nese Proposition Bank (CPB) (Xue and Palmer, 2003), which is a Chinese corpus annotated with semantic role labels. Traditional approaches to Chinese SRL often extract a large number of handcrafted features from the sentence, even its parse tree, and feed these features to statistical classifiers such as CRF, MaxEnt and SVM (Sun and Jurafsky, 2004; Xue, 2008; Ding and Chang, 2008; Ding and Chang, 2009; Sun, 2010). However, these methods suf- fer from three major problems. Firstly, their per- formances are heavily dependent on feature engi- Figure 1: A sentence with semantic roles labeled from CPB. neering, which needs domain knowledge and la- borious work of feature extraction and selection. Secondly, although sophisticated features are de- signed, the long-range dependencies in a sentence can hardly be modeled. Thirdly, a specific anno- tated dataset is often limited in its scalability, but the existence of heterogenous resource, which has very different semantic role labels and annotation schema but related latent semantic meaning, can alleviate this problem. However, traditional meth- ods cannot relate distinct annotation schemas and introduce heterogeneous resource with ease. Concerning these problems, in this paper, we propose bidirectional recurrent neural network (RNN) with long-short-term memory (LSTM) to solve the problem of Chinese SRL. Our approach makes the following contributions: • We formulate Chinese SRL with bidirection- al LSTM RNN model. With bidirectional RNN, the dependencies in a sentence from both directions can be captured, and with L- STM architecture, long-range dependencies can be well modeled. The test results on the bechmark dataset CPB show a significant im- provement over the state-of-the-art methods. • Compared with previous work that …

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