Journal
INFORMATION PROCESSING & MANAGEMENT
Volume 57, Issue 6, Pages -Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.ipm.2020.102305
Keywords
Natural language processing; Information extraction; Court record document; Neural networks; Joint model
Funding
- National Natural Science Foundation of China [61702121, 61772378]
- Research Foundation of Ministry of Education of China [18JZD015]
- Key Project of State Language Commission of China [ZDI135112]
- Guangdong Basic and Applied Basic Research Foundation of China [2020A151501705]
- Science and Technology Project of Guangzhou [201704030002]
Ask authors/readers for more resources
Information extraction is one of the important tasks in the field of Natural Language Processing (NLP). Most of the existing methods focus on general texts and little attention is paid to information extraction in specialized domains such as legal texts. This paper explores the task of information extraction in the legal field, which aims to extract evidence information from court record documents (CRDs). In the general domain, entities and relations are mostly words and phrases, indicating that they do not span multiple sentences. In contrast, evidence information in CRDs may span multiple sentences, while existing models cannot handle this situation. To address this issue, we first add a classification task in addition to the extraction task. We then formulate the two tasks as a multi-task learning problem and present a novel end-to-end model to jointly address the two tasks. The joint model adopts a shared encoder followed by separate decoders for the two tasks. The experimental results on the dataset show the effectiveness of the proposed model, which can obtain 72.36% F1 score, outperforming previous methods and strong baselines by a large margin.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available