Abstract
Most researches to SRL focus on English. It is still a challenge to improve the SRL performance of other language. In this paper, we introduce a two-pass approach to do Chinese SRL with a Recurrent Neural Network (RNN) model. We use English Proposition Bank (EPB) to improve the performance of Chinese SRL. Experimental result shows a significant improvement over the state-of-the-art methods on Chinese Proposition Bank (CPB), which reaches 78.39 % F1 score.
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Notes
- 1.
Conditional clause is seen as a semantic role in the Chinese but not in the English.
- 2.
PKU bilingual corpus is developed by Peking University, it is a English-Chinese parallel corpus. It contains 807,500 aligned English-Chinese sentence pairs and is available by licensing.
- 3.
We use GIZA++ to get the translation probability from PKU bilingual corpus, GIZA++ can be download in http://code.google.com/p/giza-pp/downloads/list.
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Acknowledgments
This work is supported by National Key Basic Research Program of China (2014CB340504) and National Natural Science Foundation of China (61273318).
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Li, T., Li, Q., Chang, B. (2016). Improving Chinese Semantic Role Labeling with English Proposition Bank. In: Sun, M., Huang, X., Lin, H., Liu, Z., Liu, Y. (eds) Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data. NLP-NABD CCL 2016 2016. Lecture Notes in Computer Science(), vol 10035. Springer, Cham. https://doi.org/10.1007/978-3-319-47674-2_1
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