Unsupervised and Constrained Dirichlet Process Mixture Models for Verb 

Speaker: Andreas Vlachos Computer Laboratory Cambridge 

University Abstract:  

In this work we apply Dirichlet Process Mixture Models (DPMMs) to a learning task in natural language processing (NLP): lexical-semantic verb clustering. Furthermore, we propose a novel method of guiding the DPMM towards a particular clustering solution using pairwise constraints. The quantitative and qualitative evaluation performed highlights the benefits of both standard and constrained DPMMs compared to previously used approaches

Intelectual Propety Management

by Steve Gotz

Patents

trade marks

designs

copyright

know-how

requeriments:

novel

inventive

industrial aplication

Important to get a Lab Notebook

the pattens do not need to be perfect

fixing the contract outreach with DCU

Tutorial on Grammar induction

key words:

Bayesian learning

signal '--> concept

making up a grammar:

we start with a grammar where each rule describe one of the sentence of the corpus: so there are the same number of non terminal nodes/symbols as a terminal nodes or words at the corpus.

2 operations:

Merge - > reduce the number of nodes.

Chinking - > increase the number of nodes.

Selecting operations the grammar become more and more open.

The objective is to compare trees analysis, so the accuracy is measure as how near get the tree analysis of the emerge parser compared with the tree of a stablish parser.

(??? not very clear point)

for artificial grammars it give a very good results.

For real data sets not very good.

Full information:

www.webexperiment.nl

http://turing.science.uva.nl/~jzuidema/teaching/dublin09/

First day at DCU, Grammar induction

Spainish Lecture

First spanish lecture 1.5h

searching for:

summer schools: --> machine learning in cambridge 

http://mlg.eng.cam.ac.uk/mlss09/registration.htm

Tutorial Lectures Speaker Talk Title Christopher Bishop Introduction to Bayesian Inference Zoubin Ghahramani Graphical Models David MacKay Information Theory Iain Murray Markov Chain Monte Carlo Bernhard Schölkopf Kernel Methods Michael Littman Reinforcement Learning Carl Edward Rasmussen Gaussian Processes John Shawe-Taylor Learning Theory Lieven Vandenberghe Convex Optimization Andrew Blake Computer Vision David Blei Topic Models Tom Minka Approximate Inference Yee Whye Teh Nonparametric Bayesian Models Josh Tenenbaum Machine Learning and Cognitive Science Geoffrey Hinton TBA Simon Godsill Particle Filters Phil Dawid Causality Thomas Hofmann Information Retrival Peter Orbanz Foundations of Nonparametric Bayesian Methods Michael Jordan TBA

Practicals Speaker Course Title Joaquin Quiñonero Candela Gaussian Processes Andrew Fitzgibbon Computer Vision John Winn & Tom Minka Probabilistic Programming Iain Murray Markov Chain Monte Carlo

preparing first spanish lecture.

THIMESHEET FOR RESEARCH CONTRACTS

sematic role labeling

2h meeting

sematic role labeling:

how to predict the semantic role relation:

predict the name of the relation.

predict the relation it-selve

1.- tree distance knn

matching with the k neigbours the best fit.

2.- three kernels:

analizing how looks each posible subtree as a target

some  preliminar concepts:

http://es.wikipedia.org/wiki/Función_de_densidad

http://es.wikipedia.org/wiki/Función_gamma

http://es.wikipedia.org/wiki/Distribución_ji-cuadrado

Online Passive-Agressive Algorithms

Online Passive-Agressive Algorithms

online classification,

regrsion

uniclass problems.

singles algorithmic framework

trying to make work google book downloader:

http://www.codeplex.com/GoogleBookDownloader

Weka in matlab:

http://www.mathworks.com/matlabcentral/fileexchange/19260

looking source code on internet:

knn in matlab:

http://www.mathworks.com/matlabcentral/fileexchange/19345

in c++

http://cmp.felk.cvut.cz/cmp/software/stprtool/

Version 2.09, 15-aug-2008 (latest): Interface to XTAL regression package was added the STPRtool. XTAL implements the following regression methods:

• Projection pursuit regression (SMART)
• Multilayer perceptron
• Multivariate adaptive regression splines
• k-nearest neighbors and Constrained topological mapping
• Constrained topological mapping

reading acm new

updating cv

fixing problems with

ACM & CNGL

Estady english 1h

pick up maps for workshop