Part-of-Speech Enhanced Context Recognition

Rasmus Elsborg Madsen, Jan Larsen, Lars Kai Hansen

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    Language independent `bag-of-words' representations are surprisingly efective for text classi¯cation. In this communi- cation our aim is to elucidate the synergy between language inde- pendent features and simple language model features. We consider term tag features estimated by a so-called part-of-speech tagger. The feature sets are combined in an early binding design with an optimized binding coefficient that allows weighting of the relative variance contributions of the participating feature sets. With the combined features documents are classi¯ed using a latent semantic indexing representation and a probabilistic neural network classi- fier. Three medium size data-sets are analyzed and we find consis- tent synergy between the term and natural language features in all three sets for a range of training set sizes. The most significant en- hancement is found for small text databases where high recognition rates are possible.
    Original languageEnglish
    Title of host publicationProceedings of IEEE Workshop on Machine Learning for Signal Processing XIV
    PublisherIEEE Press
    Publication date2004
    ISBN (Print)0-7803-8608-4
    Publication statusPublished - 2004
    Event14th IEEE Signal Processing Society Workshop Machine Learning for Signal Processing, 2004. -
    Duration: 1 Jan 2004 → …


    Conference14th IEEE Signal Processing Society Workshop Machine Learning for Signal Processing, 2004.
    Period01/01/2004 → …

    Bibliographical note

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    • text mining
    • context recognition
    • latent space


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