Mozilla are looking for a Quantitative user researcher which sounds cool. The emphasis on user research sounds right up my street, particularly the need for mastery of experimental design and statistical analysis. It kind of takes me back to my PhD and work on SalStat (still going strong).

The problem is my covering letter. Can anyone here tell me what style of covering letters are preferred? Long and detailed explaining why I meet each of the requirements? The standard 3 paragraph ["intro", "I'm cool", "thanks"]? Or some combination in between?

In the meantime, I've released Roistr which does some basic semantic analysis / text analytics stuff. I put up some demos but it's hard to really show how useful this thing is. It's based on the open source Gensim toolkit along with numpy and scipy.

Scipy sounds like it's going places. Travis Oliphant recently announced an initiative to bring it to big data properly. I have an idea of what he means and it would be very cool.

Does anyone have any Google Plus invites that they could send (one) to me?

In other news, wife, daughter and I are off to the Philippines for 5 weeks and hoping to get some start-up work moving over there. UX is in demand at the moment so it's a good time to be around.

I've also been looking up versions of principle components analysis in Python and found these:

Modular Toolkit for Data Processing

PCA module for Python

A version in MatPlotLib

All the linguistic stuff I've been doing lately is making my head spin but it's coming together.

Lots happening: I've been building a semantic relevance engine - something that can accurately determine the semantic similarity of 2 text documents and it's working reasonably well. Working completely untrained, I'm getting accuracies of well above 0.8 and often above 0.9. Obviously 1.0 is the ideal but even human judgements rarely get above 0.9 with the corpora I've been using for this.

The good thing is that I appear to be discovering new stuff almost every day about how documents are understood. There are some approaches I've used that I've not read about in the literature so there might be some useful stuff for the world here.

However my aim is to make a web service around this. And it's all based on open source software (Python, numpy, Scipy, Gensim etc) which is perfect. There is proprietary knowledge used, however: the corpora, how it's prepared and the architecture of the engine; but that will all come publicly out soon enough.

Log Entropy models

I had problems when I last upgraded to 0.7.8 of Gensim. The main issue was that the package I imported wasn't necessarily the one used: quite often, it seemed as though the top level would be from one install whereas another import would be from somewhere else. The net result was that parts of my software were looking for an id2word method in a dictionary where there were none before.

However, I still want to try 0.7.8 if I can and I found a way. I downloaded and untarred it, and renamed it 'gensim078'. Then, I went and changed each 'from gensim import *' statement to 'from gensim078 import *' which seems to be doing the trick. I'm sure there are better ways to do it but this is working for me so I'm happy.

The advantages are that a) it's faster particularly for similarity calculations, and b) I now have access to the Log Entropy model which I'm building for G1750.

Later tonight, I'll adjust the dictionary and begin pruning words that appear across lots of documents to see if that improves the focus. The program does seem a little 'fuzzy' as it is but that is quite a human characteristic so I'm not too worried. However, it will help me explore vector models and understand them better myself.

Although the results of the word-pair semantic association task were poor, I'm not dismayed (too much!) because my whole construction is not perfect and there is lots of room for improvement. The task is also useful as it gives me an indication of accuracy by another means to the 20NG categorisation task. When I create a new corpus, I should ideally subject it to a battery of tests designed to test different things. With the results of these, I can work out whether the corpus is heading in the right direction or not. It's all good to have these tools even if (initially) not going how I wanted them to.

I'm turning into a perfectionist. I really need to release something useful before I refine... Release early, release often...

I've been having lots of fun lately with Gensim, a Python framework for vector space modelling. It includes fun stuff like latent semantic analysis, latent dirichlet allocation and other goodies. Allied with NLTK, this makes a very formidable Python- based NLP framework.

My tasks are sorting newsgroup posts into correct groups and I've achieved a reasonable level of accuracy (0.92) which isn't bad given that it's entirely dependent upon content. However, most analyses are showing lower accuracies (0.70+) which isn't bad but not far away enough from chance performance to be taken realistically. However, there are a few ways to improve this and I'm conducting an enormous number of experiments to get an effective mental model of how vector space models work.

This is all the beginning of constructing a relevance engine which I'm sure will be useful to some people.

Great fun!