A comparative study of automated legal text classification using random forests and deep learning-Reference-Cited by-同舟云学术

A comparative study of automated legal text classification using random forests and deep learning

Published:2022-03 Issue:2 Volume:59 Page:102798
ISSN:0306-4573
Container-title:Information Processing & Management
language:en
Short-container-title:Information Processing & Management

Author:

Chen Haihua^ORCID,Wu Lei,Chen Jiangping,Lu Wei,Ding Junhua

Publisher

Elsevier BV

Subject

Library and Information Sciences,Management Science and Operations Research,Computer Science Applications,Media Technology,Information Systems

Reference56 articles.

1. DocBERT: BERT for document classification;Adhikari,2019

2. Predicting judicial decisions of the European court of human rights: A natural language processing perspective;Aletras;PeerJ Computer Science,2016

3. Semantic text classification: A survey of past and recent advances;Altınel;Information Processing & Management,2018

4. Boella, G., Di Caro, L., & Humphreys, L. (2011). Using classification to support legal knowledge engineers in the Eunomos legal document management system. In Fifth international workshop on juris-informatics.

5. Random forests;Breiman;Machine Learning,2001

Cited by 84 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Is Deep Learning a Better Option than Random Forest for Encrypted Traffic Classification?;2024 IEEE 49th Conference on Local Computer Networks (LCN);2024-10-08

2. Extending limited datasets with GAN-like self-supervision for SMS spam detection;Computers & Security;2024-10

3. Semi-supervised regression with label-guided adaptive graph optimization;Applied Intelligence;2024-08-24

4. Applications of Deep Learning Techniques;Electronics;2024-08-23

5. Event is more valuable than you think: Improving the Similar Legal Case Retrieval via event knowledge;Information Processing & Management;2024-07