Abstract
This paper proposes a novel transition-based algorithm for character-level Chinese dependency parsing that straightforwardly models the dependency tree in a top-down manner. Based on the stack-pointer parser, we joint Chinese word segmentation, part-of-speech tagging, and dependency parsing in a new way. We recursively build the character-based dependency tree from root to leaf in a depth-first fashion, by searching for candidate dependents through the sentence and predicting relation type at each step. We introduce intra-word dependencies into the relation types for word segmentation, and the inter-word dependencies with POS tags for part-of-speech tagging. Since the top-down model provides a global view of an input sentences, the information of the whole sentence and all previously generated arcs are available for action decisions, and all characters of the sentence are considered as candidate dependencies. Experimental results on the Penn Chinese Treebank (CTB) show that the proposed model outperformed existing neural joint parsers by 0.81% on dependency parsing, and achieved the F1-scores of 95.97%, 91.72%, 80.25% for Chinese word segmentation, part-of-speech tagging, and dependency parsing.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Ma, X., Liu, Z., Hovy, E.: Unsupervised ranking model for entity coreference resolution. In: Proceedings of NAACL-2016, San Diego, California, USA (2016)
Bastings, J., Titov, I., Aziz, W., Marcheggiani, D., Simaan, K.: Graph convolutional encoders for syntax-aware neural machine translation. In: Proceedings of EMNLP-2017. Copenhagen, Denmark, pp. 1957–1967 (2017)
Peng, N., Poon, H., Quirk, C., Toutanova, K., Yih, W.: Cross-sentence n-ary relation extraction with graph lstms. Trans. Assoc. Comput. Linguist. 5, 101–115 (2017)
Hatori, J., Matsuzaki, T., Miyao, Y., Tsujii, J.: Incremental joint approach to word segmentation, POS tagging, and dependency parsing in Chinese. In: Proceedings of ACL, pp. 1045–1053. Association for Computational, Jeju (2012)
Zhang, M., Zhang, Y., Che, W., Liu, T.: Character-level Chinese dependency parsing. In: Proceedings of ACL, pp. 1326–1336. Association for Computational, Baltimore (2012)
Kurita, S., Kawahara, D., Kurohashi, S.: Neural joint model for transition-based Chinese syntactic analysis. In: Proceedings of ACL, pp. 1204–1214. Association for Computational, Vancouver (2018)
Li, H., Zhang, Z., Ju, Y., Zhao, H.: Neural character-level dependency parsing for Chinese. In: Proceedings of 32nd AAAI Conference on Artificial Intelligence (2018)
Mcdonald, R., Nivre, J.: Analyzing and integrating dependency parsers. Comput. Linguist. 37(1), 197–230 (2011)
Ma, X., Hu, Z., Liu, J., Peng, N., Neubig, G., Hovy, E.: Stack-pointer networks for dependency parsing. In: Proceedings of ACL, pp. 1403–1414. Association for Computational, Melbourne (2018)
Vinyals, O., Fortunato, M., Jaitly, N.: Pointer networks. In: Advances in Neural Information Processing Systems, pp. 2692–2700 (2015)
Fernández-González, D., Gómez-Rodríguez, C.: Left-to-right dependency parsing with pointer networks. arXiv preprint arXiv:1903.08445 (2019)
Zheng, X., Chen, H., Xu, T.: Deep learning for Chinese word segmentation and POS tagging. In: Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing, pp. 647–657 (2014)
Shao, Y., Hardmeier, C., Tiedemann, J., Nivre, J.: Character-based joint segmentation and POS tagging for Chinese using bidirectional RNN-CRF. arXiv preprint arXiv:1704.01314 (2017)
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)
Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, pp. 5998–6008 (2017)
Luong, M.T., Pham, H., Manning, C.D.: Effective approaches to attention-based neural machine translation. arXiv preprint arXiv:1508.04025 (2015)
Dozat, T., Manning, C.D.: Deep biaffine attention for neural dependency parsing. arXiv preprint arXiv:1611.01734 (2016)
Jiang, W., Huang, L., Liu, Q., Lü, Y.: A cascaded linear model for joint Chinese word segmentation and part-of-speech tagging. In: Proceedings of ACL. Association for Computational Linguistics, Columbus (2008)
Pascanu, R., Mikolov, T., Bengio, Y.: On the difficulty of training recurrent neural networks. In: International Conference on Machine Learning, pp. 1310–1318 (2013)
Dyer, C., Ballesteros, M., Ling, W., Matthews, A., Smith, N.A.: Transition-based dependency parsing with stack long short-term memory. arXiv preprint arXiv:1505.08075 (2015)
Acknowledgments
The authors are supported by the National Nature Science Foundation of China (Nos. 61876198, 61370130 and 61473294), the Fundamental Research Funds for the Central Universities (2015JBM033), and the International Science and Technology Cooperation Program of China (No. K11F100010).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Chen, Y., Liu, H., Zhang, Y., Xu, J., Chen, Y. (2019). A Top-Down Model for Character-Level Chinese Dependency Parsing. In: Sun, M., Huang, X., Ji, H., Liu, Z., Liu, Y. (eds) Chinese Computational Linguistics. CCL 2019. Lecture Notes in Computer Science(), vol 11856. Springer, Cham. https://doi.org/10.1007/978-3-030-32381-3_54
Download citation
DOI: https://doi.org/10.1007/978-3-030-32381-3_54
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-32380-6
Online ISBN: 978-3-030-32381-3
eBook Packages: Computer ScienceComputer Science (R0)