Advanced approximate sentence matching in Python

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

Fuzzy match sentences in Python

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

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

By |2012-10-31T09:37:55-04:00October 31st, 2012|Consulting, Law, Technology|0 Comments

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