Abstract
Joint entity and relation extraction for legal documents is an important research task of judicial intelligence informatization, aiming at extracting structured triplets from rich unstructured legal texts. However, the existing methods for joint entity relation extraction in legal judgment documents often lack domain-specific knowledge, and are difficult to effectively solve the problem of entity overlap in legal texts. To address these issues, we propose a joint entity and relation extraction for legal documents method based on table filling. Firstly, we construct a legal dictionary with knowledge characteristics of the judicial domain based on the characteristics of judicial document data and incorporate it into a text encoding representation using a multi-head attention mechanism; Secondly, we transform the joint extraction task into a table-filling problem by constructing a two-dimensional table that can express the relation between word pairs for each relation separately and designing three table-filling strategies to decode the triples under the corresponding relations. The experimental results on the information extraction dataset in “CAIL2021” show that the proposed method has a significant improvement over the existing baseline model and achieves significant results in addressing the complex entity overlap problem in legal texts.
Project supported by the National Natural Science Foundation of China (62176145).
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Zhang, H., Qin, H., Zhang, G., Wang, Y., Li, R. (2024). Joint Entity and Relation Extraction for Legal Documents Based on Table Filling. In: Luo, B., Cheng, L., Wu, ZG., Li, H., Li, C. (eds) Neural Information Processing. ICONIP 2023. Communications in Computer and Information Science, vol 1966. Springer, Singapore. https://doi.org/10.1007/978-981-99-8148-9_17
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DOI: https://doi.org/10.1007/978-981-99-8148-9_17
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