John Phillips
نویسنده
چکیده
Legal analysis is a multistep process that performs the complex tasks of identifying real world activities through the lens of a predefined legal code and sets of laws. Based on a brief understanding of an issue a legal professional must make a recommendation on where to apply limited researching resources. We employ Naïve Bayes models in a parallel, nonmutually exclusive alignment towards the generic first phase of legal analysis: legal issue spotting. We do this by training separate supervised learning models by first extracting all references to the U.S. Legal Code from each case opinion of the U.S. Federal Reporter, 3rd Series (F.3d) and treating it as a binary label to that U.S.C. section. We then use the same feature set for all binary classification models selected from the most frequently occuring words in the opinions of the U.S. Federal Reporter, 3rd Series (F.3d) after NLP techniques are applied. To test we use 80/20 hold out validation testing and find mixed but not uninspiring results.
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Three New Systematic Approaches for Computing Heffron-Phillips Multi-Machine Model Coefficients (RESEARCH NOTE)
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ACKNOWLEDGEMENTS TABLE OF CONTENTS LIST OF FIGURES LIST OF TABLES
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