Abstract
Existing approaches to Chinese semantic role labeling (SRL) mainly adopt deep long short-term memory (LSTM) neural networks to address the long-term dependencies problem. However, deep LSTM networks cannot address the vanishing gradient problem properly. In addition, the complexity of the Chinese language, as a hieroglyphic language, decreases the performance of traditional SRL approaches to Chinese SRL. To address these problems, this paper proposes a new approach with a deep bidirectional highway LSTM network. The performance of the proposed approach is further improved by introducing the conditional random fields (CRFs) constraints and part-of-speech (POS) feature since POS tags are the classes of formal equivalents of words in linguistics. The experimental results on the commonly used Chinese Proposition Bank dataset show that the proposed approach outperforms existing approaches. With an easily acquired and reliable POS feature for practical applications, the proposed approach substantially improves Chinese SRL.
Original language | English |
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Title of host publication | International Joint Conference on Neural Networks (IJCNN) 2019 |
Editors | Plamen Angelov, Manuel Roveri |
Place of Publication | Piscataway NJ USA |
Publisher | IEEE, Institute of Electrical and Electronics Engineers |
Number of pages | 6 |
ISBN (Electronic) | 9781728119854 |
ISBN (Print) | 9781728119861 |
DOIs | |
Publication status | Published - 2019 |
Event | IEEE International Joint Conference on Neural Networks 2019 - Budapest, Hungary Duration: 14 Jul 2019 → 19 Jul 2019 https://ieeexplore.ieee.org/xpl/conhome/8840768/proceeding (Proceedings) |
Conference
Conference | IEEE International Joint Conference on Neural Networks 2019 |
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Abbreviated title | IJCNN 2019 |
Country | Hungary |
City | Budapest |
Period | 14/07/19 → 19/07/19 |
Internet address |
Keywords
- Chinese language
- conditional random field
- deep bidirectional highway LSTM networks
- part-of-speech
- Semantic role labeling