An end-to-end joint model for evidence information extraction from court record document

作者：

Highlights：

• We explore a new task of information extraction, which aims to extract evidence information from count record documents in the legal domain.

• We formalize evidence information extraction task as an integration of paragraph classification and sequence labeling problem, and propose an end-to-end joint model for the task.

• Our model achieves the current best performances, outperforming previous methods and neural baseline systems by a large margin.

• The proposed model can be applied for better analyzing and understanding legal texts, avoiding a lot of manual work for experts and professionals in the legal domain.

摘要

•We explore a new task of information extraction, which aims to extract evidence information from count record documents in the legal domain.•We formalize evidence information extraction task as an integration of paragraph classification and sequence labeling problem, and propose an end-to-end joint model for the task.•Our model achieves the current best performances, outperforming previous methods and neural baseline systems by a large margin.•The proposed model can be applied for better analyzing and understanding legal texts, avoiding a lot of manual work for experts and professionals in the legal domain.

论文关键词：Natural language processing,Information extraction,Court record document,Neural networks,Joint model

论文评审过程：Received 7 February 2020, Revised 14 May 2020, Accepted 15 May 2020, Available online 29 May 2020, Version of Record 29 May 2020.

论文官网地址：https://doi.org/10.1016/j.ipm.2020.102305