Natural Language Processing and Machine Learning, some pointers

October 14th, 2012  |  Published in data

I’ve been doing a lot of natural-language machine-learning work both for clients and in side-projects recently. Mark Needham asked me on Twitter for some pointers to good introductory material. Here’s what I wrote for him:

Nearly all text processing starts by transforming text into vectors:
http://en.wikipedia.org/wiki/Vector_space_model

Often it uses transforms such as TFIDF to normalise the data and control for outliers (words that are too frequent or too rare confuse the algorithms):
http://en.wikipedia.org/wiki/Tf%E2%80%93idf

Collocations is a technique to detect when two or more words occur more commonly together than separately (e.g. “wishy-washy” in English) – I use this to group words into n-gram tokens because many NLP techniques consider each word as if it’s independent of all the others in a document, ignoring order:
http://matpalm.com/blog/2011/10/22/collocations_1/
http://matpalm.com/blog/2011/11/05/collocations_2/

When you’ve got a lot of text and you don’t know what the patterns in it are, you can run an “unsupervised” clustering using Latent Dirichlet allocation:
http://www.cs.princeton.edu/~blei/papers/Blei2012.pdf
http://www.youtube.com/watch?v=5mkJcxTK1sQ

Or if you know how your data is divided into topics, otherwise known as “labeled data”, then you can run “supervised” techniques such as training a classifier to predict the labels of new similar data. I can’t find a really good page on this – I picked up a lot in IM with my friend Ben who is writing a book coming out next year: http://blog.someben.com/2012/07/sequential-learning-book/

Here are the tools I’ve mostly been using:

Some blogs I like:

MetaOptimize Q+A is the Stack Overflow of ML: http://metaoptimize.com/qa

The Mahout In Action book is quite good and practical: http://manning.com/owen/

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