Package: collective.classification
Introductioncollective.classificationaims to provide a set of tools for automatic
document classification. Currently it makes use of theNatural Language Toolkitand features a trainable document classifier based
on Part Of Speech (POS) tagging, heavily influenced bytopia.termextract.
This product is mostly intended to be used for experimentation and
development. Currently english and dutch are supported.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:1) Term extraction can be performed to provide quick insight on what a
document is about.
2) 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.
3) Similar documents can be found based on term similarity.
4) Clustering can help you organize unclassified content into groups.How it works?At the moment there exist the following type of utilities:POS taggers, utilities for classifying words in a document
asParts 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, anaive Bayesclassifier 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.Utilities that findsimilar contentbased on the extracted terms.Clusterers, utilities that without prior knowledge of content
classification can group content into groups according to feature
similarity. At the moment NLTK’sk-meansclusterer is used.Installation & SetupBefore 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” section
and 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 not big (about 400kb) but you need the plone user to have access to its “home”. Running the
tests will also fetch more data from nltk bringing the total to about 225Mb, so not for the faint at disk space.How to use it?For a parsed document you can call the term view to display the identified
terms (just append@@termsto the url of the content to call the view).In order to use the classifier and get suggested tags for some content,
you can call@@suggest-categorieson 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.You can find similar content for some content based on its terms by
calling the@@similar-itemsview.For clustering you can just call the@@clusterizeview 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!Integration testHere, we’ll test the classifier using a sample of the Brown corpus. The Brown
corpus has a list of POS-tagged english articles which are also conveniently
categorized. The test consists of training the classifier using 20 documents
from each of the categories ‘news’,’editorial’ and ‘hobbies’. Then we’ll ask
the classifier to classify 5 more documents from each category and see what
happens.We can now start adding documents, starting with the first 20 documents in the
Brown corpus categorized as ‘news’.>>> from nltk.corpus import brown
>>> for articleid in brown.fileids(categories='news')[:20]:
...     text = " ".join(brown.words(articleid))
...     id = self.folder.invokeFactory('Document',articleid,
...                                    title=articleid,
...                                    text=text,
...                                    subject='news')Continuing with 20 documents categorized as ‘editorial’:>>> for articleid in brown.fileids(categories='editorial')[:20]:
...     text = " ".join(brown.words(articleid))
...     id = self.folder.invokeFactory('Document',articleid,
...                                    title=articleid,
...                                    text=text,
...                                    subject='editorial')And finally 20 documents categorized as ‘hobbies’:>>> for articleid in brown.fileids(categories='hobbies')[:20]:
...     text = " ".join(brown.words(articleid))
...     id = self.folder.invokeFactory('Document',articleid,
...                                    title=articleid,
...                                    text=text,
...                                    subject='hobbies')All these documents should have been parsed and indexed:>>> catalog = self.folder.portal_catalog
>>> sr = catalog.searchResults(noun_terms='state')
>>> len(sr) > 5
TrueLet’s see what terms we get for the first ‘editorial’ content:>>> browser = self.getBrowser()
>>> browser.open(self.folder.absolute_url()+'/cb01/@@terms')
>>> browser.contents
'...state...year...budget...war...danger...nuclear war...united states...'Nuclear war and United States? Scary stuff… Time to train the classifier:>>> from zope.component import getUtility
>>> from collective.classification.interfaces import IContentClassifier
>>> classifier = getUtility(IContentClassifier)
>>> classifier.train()
>>> classifier.tags()
['editorial', 'hobbies', 'news']For a start, the classifier should be pretty certain when asked about text
already classified:>>> browser.open(self.folder.absolute_url()+'/ca01/@@suggest-categories')
>>> browser.contents
'...news 100.0%...editorial 0.0%...hobbies 0.0%...'So let’s see where this gets us, by asking the classifier to categorize 5 more
documents for which we know the category. We will use the classifier’s
functions directly this time instead of adding the documents to plone and
calling the@@suggest-categoriesview. ‘News’ first:>>> classificationResult = []
>>> for articleid in brown.fileids(categories='news')[20:25]:
...     text = " ".join(brown.words(articleid))
...     id = self.folder.invokeFactory('Document',articleid,
...                                    text=text)
...     uid = self.folder[id].UID()
...     classificationResult.append(classifier.classify(uid))
>>> classificationResult
['news', 'news', 'news', 'news', 'news']Let’s see how we do with ‘editorials’>>> classificationResult = []
>>> for articleid in brown.fileids(categories='editorial')[20:25]:
...     text = " ".join(brown.words(articleid))
...     id = self.folder.invokeFactory('Document',articleid,
...                                    text=text)
...     uid = self.folder[id].UID()
...     classificationResult.append(classifier.classify(uid))
>>> classificationResult
['editorial', 'editorial', 'editorial', 'editorial', 'editorial']That’s excellent! What about ‘hobbies’?>>> classificationResult = []
>>> for articleid in brown.fileids(categories='hobbies')[20:25]:
...     text = " ".join(brown.words(articleid))
...     id = self.folder.invokeFactory('Document',articleid,
...                                    text=text)
...     uid = self.folder[id].UID()
...     classificationResult.append(classifier.classify(uid))
>>> classificationResult
['hobbies', 'hobbies', 'editorial', 'hobbies', 'hobbies']Not so bad! Overall: we got 14/15 right…Let’s now pick again the first editorial item and see which documents are
similar to it based on the terms we extracted:>>> browser.open(self.folder['cb01'].absolute_url()+'/@@similar-items')The most similar item (a Jaccard index of ~0.2) is
cb15:>>> browser.contents
'...cb15...0.212121212121...'Let’s see what their common terms are:>>> cb01terms = catalog.searchResults(
... UID=self.folder['cb01'].UID())[0].noun_terms[:20]
>>> cb15terms = catalog.searchResults(
... UID=self.folder['cb15'].UID())[0].noun_terms[:20]
>>> set(cb01terms).intersection(set(cb15terms))
set(['development', 'state', 'planning', 'year', 'area'])which is fine, since both documents talk about development and budget
planning…What about stats? We can call the@@statsview to find out…>>> self.setRoles('Manager')
>>> browser.open(self.folder.absolute_url()+'/@@classification-stats')
>>> browser.contents
'...state...True...editorial:hobbies...5.0...'which basically tells us, that if the word ‘state’ is present the classifier
gives 5 to 1 for the content to be in the ‘editorial’ category rather than the
‘hobbies’ category
Changelog
=========0.1b2Removed the persistent noun storage altogether. Now noun and noun-phrase
terms are stored directly in the catalog using plone.indexer.
[ggozad, stefan]Using BTrees instead of PersistenDict. Should make writes to ZODB lighter.
[ggozad]Noun-phrase grammar and normalization is now a property of the
language-dependent tagger.
[ggozad]Removed a lot of the control panel functionality. Not need for confusion.
[ggozad]Fixed Dutch language support.
[ggozad]0.1b1Speed gain by not utilizing the PenTreeBank tagger anymore. [ggozad]Added multi-lingual support, starting with dutch! [ggozad]No need to download all the coprora anymore. [ggozad]A lot of refactoring. Things got moved around and a lot of unnecessary code
was removed. [ggozad]We now use a Brill/Trigram/Affix tagger that is pre-trained. This
allows collective.classification to ship without all the corpora. The user
can still supply a different tagger if necessary. [ggozad]The default nltk PenTreeBank tagger is no longer used. Too slow. [ggozad]npextractor is no longer a local persistent utility. Opted for a global
non-persisted object. [ggozad]zope.lifecycle events are now used. [ggozad]Gained compatibility with plone 4. [ggozad]0.1a3Introduced IClassifiable interface. ATContentTypes are now adapted to it,
and it should be easier to add other non-AT content types or customize the
adapter. [ggozad]Handling of IObjectRemovedEvent event. [ggozad]Added a form to import sample content from the brown corpus, for debugging
and testing. [ggozad]Added some statistics information with @@classification-stats.
Displays the number of parsed documents, as well as the most useful terms.
[ggozad]Added @@terms view, allowing a user to inspect the identified terms for some
content. [ggozad]Have to specify corpus categories when training n-gram tagger. Fixes #3
[ggozad]0.1a2Made control panel more sane. Fixes #1. [ggozad]NP-extractor has become a local persistent utility. [ggozad]Renamed @@subjectsuggest to @@suggest-categories. Fixes #2. [ggozad]“memoized” term extractor. [ggozad]Added friendly types to the control panel. [ggozad]Updated documentation and dependencies to warn about yaml. [ggozad]0.1a1First public release. [ggozad]