Tag Archives: corpus

The Beginning of Python Text Processing with NLTK Cookbook

It all started with an email to the baypiggies mailing list. An acquisition editor for Packt was looking for authors to expand their line of python cookbooks. For some reason I can’t remember, I thought they wanted to put together a multi-author cookbook, where each author contributes a few recipes. That sounded doable, because I’d already written a number of articles that could serve as the basis for a few recipes. So I replied with links to the following articles:

The reply back was:

The next step is to come up with around 8-14 topics/chapters and around 80-100 recipes for the book as a whole.

My first reaction was “WTF?? No way!” But luckily, I didn’t send that email. Instead, I took a couple days to think it over, and realized that maybe I could come up with that many recipes, if I broke my knowledge down into small pieces. I also decided to choose recipes that I didn’t already know how to write, and use them as motivation for learning & research. So I replied back with a list of 92 recipes, and got to work. Not surprisingly, the original list of 92 changed significantly while writing the book, and I believe the final recipe count is 81.

I was keenly aware that there’d be some necessary overlap with the original NLTK book, Natural Language Processing with Python. But I did my best to minimize that overlap, and to present a different take on similar content. And there’s a number of recipes that (as far as I know) you can’t find anywhere else, the largest group of which can be found in Chapter 6, Transforming Chunks and Trees. I’m very pleased with the result, and I hope everyone who buys the book is too. I’d like to think that Python Text Processing with NLTK 2.0 Cookbook is the practical companion to the more teaching oriented Natural Language Processing with Python.

If you’d like a taste of the book, checkout the online sample chapter (pdf) Chapter 3, Custom Corpora, which details how many of the included corpus readers work, how to use them, and how to create your own corpus readers. The last recipe shows you how to create a corpus reader on top of MongoDB, and it should be fairly easy to modify for use with any other database.

Packt has also published two excerpts from Chapter 8, Distributed Processing and Handling Large Datasets, which are partially based on those original 2 articles: