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Prose And Poetry Classification And Boundary Detection Using Word Adjacency Network Analysis

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  • RANZIVELLE MARIANNE ROXAS

    (National Institute of Physics, University of the Philippines Diliman, Quezon City 1101, Philippines)

  • GIOVANNI TAPANG

    (National Institute of Physics, University of the Philippines Diliman, Quezon City 1101, Philippines)

Abstract

Word adjacency networks constructed from written works reflect differences in the structure of prose and poetry. We present a method to disambiguate prose and poetry by analyzing network parameters of word adjacency networks, such as the clustering coefficient, average path length and average degree. We determine the relevant parameters for disambiguation using linear discriminant analysis (LDA) and the effect size criterion. The accuracy of the method is74.9 ± 2.9%for the training set and73.7 ± 6.4%for the test set which are greater than the acceptable classifier requirement of 67.3%. This approach is also useful in locating text boundaries within a single article which falls within a window size where the significant change in clustering coefficient is observed. Results indicate that an optimal window size of 75 words can detect the text boundaries.

Suggested Citation

  • Ranzivelle Marianne Roxas & Giovanni Tapang, 2010. "Prose And Poetry Classification And Boundary Detection Using Word Adjacency Network Analysis," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 21(04), pages 503-512.
    • Handle: RePEc:wsi:ijmpcx:v:21:y:2010:i:04:n:s0129183110015257
    DOI: 10.1142/S0129183110015257
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    Cited by:

    1. Diego R Amancio, 2015. "Probing the Topological Properties of Complex Networks Modeling Short Written Texts," PLOS ONE, Public Library of Science, vol. 10(2), pages 1-17, February.

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