In our last post, we went over a range of options to perform approximate sentence matching in Python, an import task for many natural language processing and machine learning tasks. To begin, we defined terms like: tokens: a word, number, or other "discrete" unit of text. stems: words that have had their "inflected" pieces removed based on
Four years ago, Dan Katz and I began working on a project to measure the complexity of the law. Its genesis was, in every sense, an accident; in order to properly identify citations to the IRC in our VTR empirical review of U.S. Tax Court decisions, we had to deal with the informal, non-Blue
Back in 2011, I covered the relative performance difference of the most popular libraries for text processing in R and Python. In case you can't guess the answer, Python and NLTK won by a significant margin over R and tm. Text processing with R seemed simple on paper, but performance and flexibility limitations have
Fellow Computational Legal Studies blogger and MSU law prof Dan Katz invited me to give an expert guest lecture for his e-Discovery seminar. This seminar, taught jointly with Professor Candeub, is an excellent example of MSU's strategic pivot to deliver practical, 21st-century skills to their students. The goal of the talk was to provide
