Research Publications

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Jaume Nualart, Wray Buntine, Mark Reid
NICTA Machine Learning retreat 2014 - March 2014
Kar Wai Lim, Changyou Chen, Wray Buntine
Twitter data is extremely noisy - each tweet is short, unstructured and with informal language, a challenge for current topic modeling. However, tweets are accompanied by extra information such as authorship, hashtags and user-follower networks. ...
NIPS2013 Topic Model workshop - December 2013
Rishabh Mehrotra, Scott Sanner, Wray Buntine, Lexing Xie
Twitter: the world of 140 characters poses serious challenges to the efficacy of topic models on short, messy text. While topic models such as Latent Dirichlet Allocation (LDA) have a long history of successful application to news articles and ...
The 36th Annual ACM SIGIR Conference - July 2013
Lan Du, Wray Buntine, Mark Johnson
We present a new hierarchical Bayesian model for unsupervised topic segmentation.~This new model integrates a point-wise boundary sampling algorithm used in Bayesian segmentation into a structured topic model that can capture a simple hierarchical ...
2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies - June 2013
Paul Malcolm, Wray Buntine
Latent Dirichlet Allocation (LDA) is a scheme which may be used to estimate topics and their probabilities within a corpus of text data. The fundamental assumptions in this scheme are that text is a realisation of a stochastic generative model and that ...
DSTO - April 2013
Shengbo Guo, Scott Sanner, Thore Graepel, Wray Buntine
We extend the Bayesian skill rating system of TrueSkill to accommodate score-based match outcomes. TrueSkill has proven to be a very effective algorithm for matchmaking --- the process of pairing competitors based on similar skill-level --- in ...
European Conference on Machine Learning - September 2012
Lan Du, Wray Buntine, Huidong Jin
Topic models are increasingly being used for text analysis tasks, often times replacing earlier semantic techniques such as latent seman- tic analysis. In this paper, we develop a novel adaptive topic model with the ability to adapt topics from both ...
Empirical Methods in Natural Language Processing (EMNLP) - July 2012
Changyou Chen, Nan Ding, Wray Buntine
We develop dependent hierarchical normalized random measures and apply them to dynamic topic modeling. The dependency arises via {\em superposition}, {\em subsampling} and {\em point transition} on the underlying Poisson processes of these measures. ...
International Conference on Machine Learning (ICML) - June 2012
Wray Buntine, Marcus Hutter
The two parameter Poisson-Dirichlet Process (PDP), a generalisation of the Dirichlet Process, is increasingly being used for probabilistic modelling in discrete areas such as language technology, bioinformatics, and image analysis. There is a rich ...
ARXIV - February 2012
David Newman, Edwin Bonilla, Wray Buntine
Topic models have the potential to improve search and browsing by extracting useful semantic themes from web pages and other text documents. When learned topics are coherent and interpretable, they can be valuable for faceted browsing, results set ...
Neural Information Processing Systems (NIPS) - December 2011