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The charge prediction task is to determine appropriate charges for a given case, which is helpful for legal assistant systems where the user input is fact description.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Later among the works it cites.
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Later among the works it cites.
A general approach for predicting the behavior of the supreme court of the united states
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Analyzing the extraction of relevant legal judgments using paragraph-level and citation information
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COLIEE-14
Mi-Young Kim, Randy Goebe, and Ken Satoh. 2014a
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Legal question answering using ranking svm and syntactic/semantic similarity
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