Topic Identification in Dynamical Text by Extracting Minimum Complexity Time Components

The problem of analysing dynamically evolving textual data has recently arisen. An example of such data is the discussion appearing in Internet chat lines. In this paper a recently introduced source separation method, termed as complexity pursuit, is applied to the problem. The method is a generalisation of projection pursuit to time series and it is able to use both spatial and temporal dependency information in separating the topics of the discussion. 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. The complexity pursuit method is compared to several ICA-type algorithms for time series.