Status report 2006.12.18.

I've told my teacher that on the long run, I want to implement all 4 scenarios, but what I'll do in short term depends on what he demands He told me to implement 2 of my choice.

For the first one, I've chosen the spam clustification, because it seemed to be trivial... Well... Let's see what happened

• I've asked for the full flexwiki.com repository on the FlexWikiMailingList, and CraigAndera was so kind to give it to me in few hours. A 50MB zip file with 36 471 files 1564 actual wikipages and the rest are the older versions (including the deleted topics').
• I've written an Excel macro to read in the filenames and determine which is spam and which is not
• If a version of a topic contains one word, "delete" it means it was deleted, so the previous version was a spam
• (1) I assume that if a topic is modified and not deleted, than it wasn't spam, so the version before the latest is a not spam version.
• So I got 2885 spam and 1258 notspam samples. With an other Excel macro, I've copied these to a spam and a notspam folder.
• And now, I thought, almost ready, find a classification tool, train it and enjoy the results. NOPE
• http://kt.ijs.si/Dunja/textgarden/ this seemed to do the trick, but some links are broken, others work, but the prorgam doesn't work. No source code.
• http://www.cs.cmu.edu/~mccallum/bow/ another contestant, but "The library does not: Claim to be finished. Have good documentation. Claim to be bug-free." I've spent a day trying to compile it on cygwin with no success.
• http://amontejo.home.cern.ch/amontejo/Sections/Projects/HEPindexer/index.html this looks nice, but the software isn't published, as far as I know...
• SVMLight: http://svmlight.joachims.org/ now this is a classifier, an abstract one, doesn't read text files, you have to give it the feature-value pairs. I didn't want to do this
• TCT: https://www.cs.tcd.ie/Derek.Greene/research/tct.html this is a beautiful program, reusable java code but no classifier, just text parsing and clustering.
• there is nice javadoc, but no specification or howto, so I spent more than a day with finding out what it does, and how to run it. I'm considering writing a short spec and howto, and sending it back to the author...
• LOL: the TCT writes out a matrix in a text file, and because of my Hungarian locales, java put , instead of . in the float values. When I wanted to use the file, TCT threw a "not valid float value" exception... So I had to replace them back. BUT! In a 2 MB text file, it took 2 seconds for notepad to replace one occurrence. Even notepad++ and Word died. So I had to write a script that did it line by line...
• Right now, I've parsed my training data with TCT, and written a vbscript to convert it into SVMLight input file.
• Before trying it, I looked at the files, and realized that the "bag of words" model with stemming is not good for wiki content. Mainly because of the wikiwords - a modified split algorithm would solve the problem. But there are links too, and these are crucial in detecting spam. (2) So my heuristic is that a Ngram would by much better. So I started a new parsing, with Ngrams, and got 15000 terms. I started an other parsing, with dimension cutting: a term must occur in at least 10 docs - got 7700 terms, maybe this will be good.
• SVNLight, here I come!
• Aham, precision 38% This is not good. What's the problem? The training data contains lot's of spam. (1) is not true. I'll say now: a topic, that is very long, and not deleted is not spam. And also, too many modifications mean playpage, so I'll filter this too. Back to the Excel macro.
• Precision for the BOW training file is 45%, maybe (2) isn't true either
• Ok, found the bug: I used the termID-docID pairs as docID-termID
• I used Access to swap the docID-termID, but it's still not good...