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.
- “Predicting and Understanding Law with Machine Learning,” DC Data Science Meetup on 8/1/2016. [Meetup]
- John J. Nay. [link]
- predictgov.com [link]
- John J. Nay, “Predicting and Understanding Law-Making with Machine Learning”, arXiv:1607.02109 (2016). [arXiv]
- Kwan-Yuet Ho, “Toying with Word2Vec,” Everything about Data Analytics, WordPress (2015). [WordPress]