Abstract
Almost all the state-of-the-art methods for Character-based Chinese dependency parsing ignore the complete dependency subtree information built during the parsing process, which is crucial for parsing the rest part of the sentence. In this paper, we introduce a novel neural network architecture to capture dependency subtree feature. We extend and improve recent works in neural joint model for Chinese word segmentation, POS tagging and dependency parsing, and adopt bidirectional LSTM to learn n-gram feature representation and context information. The neural network and bidirectional LSTMs are trained jointly with the parser objective, resulting in very effective feature extractors for parsing. Finally, we conduct experiments on Penn Chinese Treebank 5, and demonstrate the effectiveness of the approach by applying it to a greedy transition-based parser. The results show that our model outperforms the state-of-the-art neural joint models in Chinese word segmentation, POS tagging and dependency parsing.
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Acknowledgments
The authors are supported by the National Nature Science Foundation of China (Contract 61370130 and 61473294), and the Beijing Municipal Natural Science Foundation (4172047).
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Liu, H., Liu, M., Zhang, Y., Xu, J., Chen, Y. (2018). Improved Character-Based Chinese Dependency Parsing by Using Stack-Tree LSTM. In: Zhang, M., Ng, V., Zhao, D., Li, S., Zan, H. (eds) Natural Language Processing and Chinese Computing. NLPCC 2018. Lecture Notes in Computer Science(), vol 11109. Springer, Cham. https://doi.org/10.1007/978-3-319-99501-4_17
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DOI: https://doi.org/10.1007/978-3-319-99501-4_17
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