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.
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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.
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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
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DOI: https://doi.org/10.1007/978-981-15-3412-6_11
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