This talk, PDF slide here, is titled “Non-parametric Methods for Unsupervised Semantic Modelling” and is really a two-hour talk derived from the HKUST talk in December. I updated it continuously during January and gave it at Helsinki, IJS, UCL, Cambridge and Oxford. It contains my simplified version of Lancelot James‘ excellent theory of generalised Indian Buffet Processes,“Poisson Latent Feature Calculus for Generalized Indian Buffet Processes,” which is on Arxiv 2014. My version explains things differently with heuristics. The talk I gave at JSI (Jozef Stefan Institute in Ljublana) on 14th Jan 2015 was recorded. The group here, with Dunja Mladenić and Marko Grobelnik, are expert in areas like Data Science and Text Mining, but they’re not into Bayesian non-parametrics, so in this version of the talk I mostly avoided the statistical details and talked more about what we did and why. The talk is up on Video Lectures.

**Abstract:** This talk will cover some of our recent work in extended topic models to serve as tools in text mining and NLP (and hopefully, later, in IR) when some semantic analysis is required. In some sense our goals are akin to the use of Latent Semantic Analysis. The basic theoretical/algorithmic tool we have for this is non-parametric Bayesian methods for reasoning on hierarchies of probability vectors. The concepts will be introduced but not the statistical detail. Then I’ll present some of our KDD 2014 paper (Experiments with Non-parametric Topic Models) that is currently the best performing topic model by a number of metrics.