PROSE AND POETRY CLASSIFICATION AND BOUNDARY DETECTION USING WORD ADJACENCY NETWORK ANALYSIS

Ranzivelle Marianne Roxas () and Giovanni Tapang
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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

International Journal of Modern Physics C (IJMPC), 2010, vol. 21, issue 04, 503-512

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.

Keywords: Text genre classification; word adjacency networks; LDA; 89.75.Fb; 89.75.-k; 89.20.Ff (search for similar items in EconPapers)
Date: 2010
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Citations: View citations in EconPapers (1)

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DOI: 10.1142/S0129183110015257

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