Topic Identification in Dynamical Text by Complexity Pursuit

Ella Bingham, Ata Kaban, M. Girolami

    Research output: Contribution to journalArticleScientificpeer-review

    34 Citations (Scopus)

    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.
    Original languageEnglish
    Pages (from-to)69-83
    JournalNeural Processing Letters
    Volume17
    Issue number1
    Publication statusPublished - 2003
    MoE publication typeA1 Journal article-refereed

    Keywords

    • chat line discussion
    • complexity pursuit
    • dynamical text
    • independent component analysis
    • time series

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