On Tuesday, I presented at the monthly DC Python meetup. My talk was an introduction to gensim, a free Python framework for topic modelling and semantic similarity using LSA/LSI and other statistical techniques. I've been using gensim on and off for several months at work, and I really appreciate its performance, clean API design, documentation, and community. (All of this is due to its creator, Radim Rehurek, who I interviewed recently.)
The presentation slides are available here. I also wrote some quick gensim example code that walks through creating a corpus, generating and transforming models, and using models to do semantic similarity. The code and slides are both also available on my github account.
Finally, I also developed a demo app to visualize semantic similarity queries. It's a Flask web app, with gensim generating data on the backend that is clustered by scipy and scikit-learn and visualized by d3.js as agglomerative and hierarchical clusters as well as a simple table and dendrogram. To make it all work in realtime, I used threading and hookbox. I call it Visularity, and it's available on github. You need to provide your own model and dictionary data to use--check out my presentation and visit radimrehurek.com/gensim/ to learn how. Comments and feedback welcome!