Later this month, I’ll be giving a keynote at a meeting on Law and Computation at the University of Houston.  As part of the talk, I’m putting together an example of how I think machine learning and structured data can help build a better legal search engine.  For the talk, I’ll be building a subset of the engine I’m proposing, focusing on tax.  There’ll be plenty of goodies – Python code using NLTK, Java code using Lucene, Mahout, and Solr, and even some discussion of a few of my recent publications like An Empirical Survey of the Population of United States Tax Court Written Decisions and A Mathematical Approach to the Study of the United States Code.  Stay tuned!