I trained a recursive neural network ( https://github.com/karpathy/char-rnn ) on a bunch of Justice Scalia's dissents from the past few years. It spits out some amusing stuff, depending on the starter text and how "adventurous" you want the output. Since it's character-based and not word-based, it makes a bunch of spelling errors (unlike Justice Scalia), but is also able to create new words (just like Justice Scalia!). Here are some samples. *** Starter text: "Justice SCALIA", random level: 0.8. Never would have expected this from a strict constructionist (check the first sentence). This one brings in same-sex marriage, constitutional interpretation, and the typical contempt-ridden air quotes. "Justice SCALIA, dissenting. The Constitution is an opinion, and so views that "[t]he Court tait the structure relations (interneline) rejectly and weands is not categorical, while all this one inference to do be not applying a nample between the
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