Abstract
Currently, Knowledge Graph is playing a crucial rule in some knowledge-based applications, such as semantic search and data integration. Due to the particularity of the vocabulary and language pattern in the Chinese legal domain, the exploration of hierarchical legal knowledge structures is still challenging. In this paper, we first explore a combination of pattern-based and linguistic-rule-based approach in helping experts to identify hypernymy relationships in large-scale traffic legal corpus. Using these relationships as ground truths, we then propose a supervised hypernymy classification of candidate term pairs using an attention-based bidirectional LSTM model, in which a global context of each candidate is defined as the feature for classification. We compare the performance of our approach with state-of-art baselines on real-world data. The evaluation results show that our approach is quite effective in finding Chinese hypernym-hyponym in the traffic legal domain.
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
Notes
References
Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473 (2014)
Bi, S., Huang, Y., Cheng, X., Wang, M., Qi, G.: Building Chinese legal hybrid knowledge network. In: Douligeris, C., Karagiannis, D., Apostolou, D. (eds.) KSEM 2019. LNCS (LNAI), vol. 11775, pp. 628–639. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-29551-6_56
Bodenreider, O.: The unified medical language system (UMLS): integrating biomedical terminology. Nucleic Acids Res. 32(suppl-1), D267–D270 (2004)
Ceccagno, A., Basciano, B.: Compound headedness in Chinese: an analysis of neologisms. Morphology 17(2), 207–231 (2007)
Dong, Z., Dong, Q., Hao, C.: HowNet and the computation of meaning (2006)
Li, S., Zhao, Z., Hu, R., Li, W., Liu, T., Du, X.: Analogical reasoning on Chinese morphological and semantic relations. arXiv preprint arXiv:1805.06504 (2018)
Miller, G.A.: Wordnet: a lexical database for English. Commun. ACM 38(11), 39–41 (1995)
Snow, R., Jurafsky, D., Ng, A.Y.: Learning syntactic patterns for automatic hypernym discovery. In: Advances in Neural Information Processing Systems, pp. 1297–1304 (2005)
Wang, C., Fan, Y., He, X., Zhou, A.: Predicting hypernym-hyponym relations for Chinese taxonomy learning. Knowl. Inf. Syst. 58, 585–610 (2018)
Wang, Q., Xu, C., Zhou, Y., Ruan, T., Gao, D., He, P.: An attention-based Bi-GRU-CapsNet model for hypernymy detection between compound entities. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1031–1035. IEEE (2018)
Xiao-Jun, M.A., Guo, J.Y., Xian, Y.T., Mao, C.L., Yan, X., Zheng-Tao, Y.U.: Entity hyponymy acquisition and organization combining word embedding and bootstrapping in special domain. Computer Science (2018)
Acknowledgement
The work is partially funded by the Judicial Big Data Research Centre, School of Law at Southeast University, and is also supported by the National Natural Science Foundation of China under grant U1736204, and the National Key Research and Development Program of China under grant 2018YFC0830201.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Gao, P., Zhang, X., Qi, G. (2020). Discovering Hypernymy Relationships in Chinese Traffic Legal Texts. In: Wang, X., Lisi, F., Xiao, G., Botoeva, E. (eds) Semantic Technology. JIST 2019. Communications in Computer and Information Science, vol 1157. Springer, Singapore. https://doi.org/10.1007/978-981-15-3412-6_11
Download citation
DOI: https://doi.org/10.1007/978-981-15-3412-6_11
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-3411-9
Online ISBN: 978-981-15-3412-6
eBook Packages: Computer ScienceComputer Science (R0)