Automated Fact-Value Distinction in Court Opinions
16 Pages Posted: 25 Jun 2018 Last revised: 26 Aug 2019
Date Written: August 2019
This paper studies the problem of automated classification of fact statements and value statements in written judicial decisions. We compare a range of methods and demonstrate that the linguistic features of sentences and paragraphs can be used to successfully classify them along this dimension. The Wordscores method by Laver et al. (2003) performs best in held out data. In an application, we show that the value segments of opinions are more informative than fact segments of the ideological direction of U.S. Circuit Court opinions.
Keywords: Court opinions, Fact, Value, Text classification, Natural language processing
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