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In this paper, we give an overview of the Legal Judgment Prediction (LJP) competition at Chinese AI and Law challenge (CAIL2018).
Predicting supreme court decisions mathematically: A quantitative analysis of the ”right to counsel” cases
Fred Kort. 1957 · 1957
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Applying correlation analysis to case prediction
Stuart S Nagel. 1963 · 1963
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Quantitative analysis of judicial processes: Some practical and theoretical applications
S Sidney Ulmer. 1963 · 1963
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Mathematical models for legal prediction
R Keown. 1980 · 1980
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Predicting supreme court cases probabilistically: The search and seizure cases, 1962-1981
Jeffrey A Segal. 1984 · 1981
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Long short-term memory
Sepp Hochreiter and Jurgen Schmidhuber. 1997 · 1997
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Exploring phrase-based classification of judicial documents for criminal charges in chinese
Chaolin Liu and Chwen Dar Hsieh. 2006 · 2006
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A review of machine learning algorithms for text-documents classification
Baharum Baharudin, Lam Hong Lee, and Khairullah Khan. 2010 · 2010
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The supreme court’s many median justices
Benjamin E Lauderdale and Tom S Clark. 2012 · 2012
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Exploiting machine learning models for chinese legal documents labeling, case classification, and sentencing prediction
Wanchen Lin, Tsung Ting Kuo, and Tung Jia Chang. 2012 · 2012
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Distributed representations of words and phrases and their compositionality
Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean. 2013 · 2013
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Learning phrase representations using rnn encoder-decoder for statistical machine translation
Kyunghyun Cho, Bart Van Merrienboer, Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio. 2014 · 2014
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Glove: Global vectors for word representation
Jeffrey Pennington, Richard Socher, and Christopher Manning. 2014 · 2014
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Recurrent convolutional neural networks for text classification
Siwei Lai, Liheng Xu, Kang Liu, and Jun Zhao. 2015 · 2015
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Document modeling with gated recurrent neural network for sentiment classification
Duyu Tang, Bing Qin, and Ting Liu. 2015 · 2015
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Learning to predict charges for criminal cases with legal basis
Bingfeng Luo, Yansong Feng, Jianbo Xu, Xiang Zhang, and Dongyan Zhao. 2017 · 2017
Later among the works it cites.
Exploring the use of text classi cation in the legal domain
Octavia Maria Sulea, Marcos Zampieri, Mihaela Vela, and Josef Van Genabith. 2017 · 2017
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Few-shot charge prediction with discriminative legal attributes
Zikun Hu, Xiang Li, Cunchao Tu, Zhiyuan Liu, and Maosong Sun. 2018 · 2018
Closest in time.
Interpretable rationale augmented charge prediction system
Xin Jiang, Hai Ye, Zhunchen Luo, WenHan Chao, and Wenjia Ma. 2018 · 2018
Closest in time.
Focal loss for dense object detection
Tsung-Yi Lin, Priyal Goyal, Ross Girshick, Kaiming He, and Piotr Dollár. 2018 · 2018
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Nikolaos Aletras, Dimitrios Tsarapatsanis, Daniel Preotiuc-Pietro, and Vasileios Lampos. 2016 · 2016
Cited alongside, same era.
Hierarchical attention networks for document classification
Zichao Yang, Diyi Yang, Chris Dyer, Xiaodong He, Alex Smola, and Eduard Hovy. 2016 · 2016
Cited alongside, same era.
Deep pyramid convolutional neural networks for text categorization
Rie Johnson and Tong Zhang. 2017 · 2017
Cited alongside, same era.
Bag of tricks for efficient text classification
Armand Joulin, Edouard Grave, Piotr Bojanowski, and Tomas Mikolov. 2017 · 2017
Cited alongside, same era.
Convolutional neural networks for sentence classification
Yoon Kim. 2014a
Cited in the paper.
Convolutional neural networks for sentence classification
Yoon Kim. 2014b
Cited in the paper.
Matthew E Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer. 2018 · 2018
Closest in time.
Cail2018: A large-scale legal dataset for judgment prediction
Chaojun Xiao, Haoxi Zhong, Zhipeng Guo, Cunchao Tu, Zhiyuan Liu, Maosong Sun, Yansong Feng, Xianpei Han, Zhen Hu, Heng Wang, et al. 2018 · 2018
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Interpretable charge predictions for criminal cases: Learning to generate court views from fact descriptions
Hai Ye, Xin Jiang, Zhunchen Luo, and Wenhan Chao. 2018 · 2018
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Legal judgment prediction via topological learning
Haoxi Zhong, Zhipeng Guo, Cunchao Tu, Chaojun Xiao, Zhiyuan Liu, and Maosong Sun. 2018 · 2018
Closest in time.