collective.classification 0.1a2

Introduction

collective.classification aims to provide a set of tools for automatic document classification. Currently it makes use of the Natural Language Toolkit and features a trainable document classifier based on Part Of Speech (POS) tagging, heavily influenced by topia.termextract. This is not a finished product and is intended to be used for experimentation and development.

What is this all about?

It’s mostly about having fun! The package is in a very early experimental stage and awaits eagerly contributions. You will get a good understanding of what works or not by looking at the tests. You might also be able to do some useful things with it: On a large site with a lot of content and tags (or subjects in the plone lingo) it might be difficult to assign tags to new content. In this case, a trained classifier could provide useful suggestions to an editor responsible for tagging content.

How it works?

At the moment there exist the following type of utilities:

• POS taggers, utilities for classifying words in a document as Parts Of Speech. Two are provided at the moment, a Penn TreeBank tagger and a trigram tagger. Both can be trained with some other language than english which is what we do here.

• Term extractors, utilities responsible for extracting the important terms from some document. The extractor we use here, assumes that in a document only nouns matter and uses a POS tagger to find those mostly used in a document. For details please look at the code and the tests.

• Content classifiers, utilities that can tag content in predefined categories. Here, a naive Bayes classifier is used. Basically, the classifier looks at already tagged content, performs term extraction and trains itself using the terms and tags as an input. Then, for new content, the classifier will provide suggestions for tags according to the extracted terms of the content.

• Clusterers, utilities that without prior knowledge of content classification can group content into groups according to feature similarity. At the moment NLTK’s k-means clusterer is used.

Installation & Setup

Before running buildout, make sure you have yaml and its python bindings installed (use macports on osx, or your package installer on linux). If nltk exists for your OS you might as well install that, otherwise it will be fetched when you run buildout.

To get started you will simply need to add the package to your “eggs” and “zcml” sections, run buildout, restart your Plone instance and install the “collective.classification” package using the quick-installer or via the “Add-on Products” section in “Site Setup”.

WARNING: Upon first time installation linguistic data will be fetched from NLTK’s repository and stored locally on your filesystem. It’s about 225Mb, so not for the faint at disk space.

After installation, you should have a control panel entry to configure the product.

• By default the product uses the Pen TreeBank tagger who is not very performant. It is a good idea to go to the term extractor configuration and change it so as to use an N-Gram tagger. Among the brown corpus categories choose the ones that seem to fit better with your content. Train the tagger. This will look for all content and perform term extraction. So go grab yourself something to drink, it will take a while.

• After training the tagger, train the classifier. This will look for any content that is tagged and train the Bayes classifier.

• By default the classifier does get re-trained every time content is added or updated. If you do not want this to happen automatically (that is if you have a site with a lot of content, and things get slow), in the control panel you can disable auto-training.

How to use it?

• In order to use the classifier and get suggested tags for some content, you can call @@suggest-categories on the content. This comes down to appending @@suggest-categories to the url in your browser. A form will come up with suggestions, choose the ones that seem appropriate and apply. You will need to have the right to edit the document in order to call the view.

• For clustering you can just call the @@clusterize view from anywhere. The result is not deterministic but hopefully helpful;). You need manager rights for this so as to not allow your users to DOS your site!

Changelog