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Computational Methods for Intelligent Information Access
Supercomputing '95 Proceedings of the 1995 ACM/IEEE conference on Supercomputing
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Modern Information Retrieval
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Introduction to Modern Information Retrieval
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Blind Source Separation Using Temporal Predictability
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Neural Computation
Probabilistic latent semantic analysis
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
A blind source separation technique using second-order statistics
IEEE Transactions on Signal Processing
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IEEE Transactions on Neural Networks
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IEEE Transactions on Neural Networks
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WI '04 Proceedings of the 2004 IEEE/WIC/ACM International Conference on Web Intelligence
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SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
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Web Intelligence and Agent Systems
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ECML'06 Proceedings of the 17th European conference on Machine Learning
XML document clustering by independent component analysis
KDXD'06 Proceedings of the First international conference on Knowledge Discovery from XML Documents
SLSFS'05 Proceedings of the 2005 international conference on Subspace, Latent Structure and Feature Selection
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SIGDIAL '12 Proceedings of the 13th Annual Meeting of the Special Interest Group on Discourse and Dialogue
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The problem of analysing dynamically evolving textual data has arisen within the last few years. An example of such data is the discussion appearin g in Internet chat lines. In this Letter a recently introduced source separation method, termed as complexity pursuit, is applied to the problem of finding topics in dynamical text and is compared against several blind separation algorithms for the problem considered. Complexity pursuit is a generalisation of projection pursuit to time series and it is able to use both higher-order statistical measures and temporal dependency information in separating the topics. Experimental results on chat line and newsgroup data demonstrate that the minimum complexity time series indeed do correspond to meaningful topics inherent in the dynamical text data, and also suggest the applicability of the method to query-based retrieval from a temporally changing text stream.