Law Prediction

On August 1, my friends and I attended a meetup host by DC Data Science, titled “Predicting and Understanding Law with Machine Learning.” The speaker was John Nay, a Ph.D. candidate in Vanderbilt University. He presented his research which is at an application of natural language processing on legal enactment documents.

His talk was very interesting, from the similarity of presidents and the chambers, to the kind of topics each party focused on. He used a variety of techniques such as Word2Vec, STM (structural topic modeling), and some common textual and statistical analysis. It is quite a comprehensive study.

His work is demonstrated at predictgov.com. His work can be found in arXiv.

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Book on NLP Annotation

It is really a pleasure to participate in DC NLP meetup group. And it was so nice to listen to Ari Chanen’s talk on his application of ensembles of Word2Vec, and Mark Lidd’s on the product of DataFission.

Before the talks, the co-organizers gave out sponsored books. I am so lucky to get James Pustejovsky’s and Amber Stubb’sĀ Natual Language Annotation for Machine Learning. It is a book on annotating NLP corpuses, and itsĀ process cycle. I never annotate the corpus, but I know it is an important task, however tedious it is.

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