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
Let's imagine you have a sentence of interest. You'd like to find all occurrences of this sentence within a corpus of text. How would you go about this? The most obvious answer is to look for exact matches of the sentence. You'd search through every sentence of your corpus, checking to see if every character of the
After a nice twitter conversation this morning, I finally got the impetus to release the source for my Congressional Bill Statistics data. You can find the source at this Github repository. I haven't taken the time to review licensing yet, but I won't be asserting anything more than CC3 Attribution on my code.
Natural Language Processing and Machine Learning for e-Discovery – Slides from guest lecture at MSU College of Law
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