Topic Identification in Dynamical Text by Complexity Pursuit |
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Authors: | Bingham Ella Kabán Ata Girolami Mark |
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Affiliation: | (1) Neural Networks Research Centre, Helsinki University of Technology, P.O. Box 5400, FIN-02015 HUT, Finland |
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Abstract: | The problem of analysing dynamically evolving textual data has arisen within the last few years. An example of such data is
the discussion appearing 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.
This revised version was published online in June 2006 with corrections to the Cover Date. |
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Keywords: | chat line discussion complexity pursuit dynamical text independent component analysis time series |
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