Understand
Artificial intelligence is being utilized in many domains as of late, and the legal system is no exception.
- However, as it stands now, the number of well-annotated datasets pertaining to legal documents from the Supreme Court of the United States (SCOTUS) is very limited for public use.
- Even though the Supreme Court rulings are public domain knowledge, trying to do meaningful work with them becomes a much greater task due to the need to manually gather and process that data from scratch each time.
- Hence, our goal is to create a high-quality dataset of SCOTUS court cases so that they may be readily used in natural language processing (NLP) research and other data-driven applications.
Built on
“Using TF-IDF to determine word relevance in document queries”, 2003
Juan Ramos · 2003
Earlier work this paper cites.
“Predicting judicial decisions of the European Court of Human Rights: a Natural Language Processing perspective”
Nikolaos Aletras, Dimitrios Tsarapatsanis, Daniel Preoţiuc-Pietro and Vasileios Lampos · 2016
Earlier work this paper cites.
Similar
“CAIL2018: A Large-Scale Legal Dataset for Judgment Prediction”, 2018
Chaojun Xiao et al · 2018
Cited alongside, same era.
“Fundamentals of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) network”
Alex Sherstinsky · 2019
Cited alongside, same era.
Then
“Granted & noted list: October term 2020 cases for argument”, 2020
Supreme of United · 2020
Later among the works it cites.
Beyond the bibliography
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