TitleChinese Semantic Role Labeling with bidirectional recurrent neural networks
AuthorsWang, Zhen
Jiang, Tingsong
Chang, Baobao
Sui, Zhifang
AffiliationKey Laboratory of Computational Linguistics, Ministry of Education, School of Electronics Engineering and Computer Science, Peking University, Collaborative Innovation Center for Language Ability, Xuzhou, China
Issue Date2015
PublisherConference on Empirical Methods in Natural Language Processing, EMNLP 2015
CitationConference on Empirical Methods in Natural Language Processing, EMNLP 2015.Lisbon, Portugal,2015/1/1.
AbstractTraditional 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.
Appears in Collections:信息科学技术学院

Files in This Work
There are no files associated with this item.

Web of Science®

Checked on Last Week


Checked on Current Time

License: See PKU IR operational policies.