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International Conference on Artificial Intelligence: Methodology, Systems, and Applications

AIMSA 2016: Artificial Intelligence: Methodology, Systems, and Applications pp 121–129Cite as

Classifying Written Texts Through Rhythmic Features

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9883))

Abstract

Rhythm analysis of written texts focuses on literary analysis and it mainly considers poetry. In this paper we investigate the relevance of rhythmic features for categorizing texts in prosaic form pertaining to different genres. Our contribution is threefold. First, we define a set of rhythmic features for written texts. Second, we extract these features from three corpora, of speeches, essays, and newspaper articles. Third, we perform feature selection by means of statistical analyses, and determine a subset of features which efficiently discriminates between the three genres. We find that using as little as eight rhythmic features, documents can be adequately assigned to a given genre with an accuracy of around 80 %, significantly higher than the 33 % baseline which results from random assignment.

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Acknowledgements

The work presented in this paper was partially funded by the EC H2020 project RAGE (Realising and Applied Gaming Eco-System) http://www.rageproject.eu/ Grant agreement No 644187.

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Authors and Affiliations

  1. Computer Science Department, University Politehnica of Bucharest, Bucharest, Romania

    Mihaela Balint, Mihai Dascalu & Stefan Trausan-Matu

  2. Research Institute for Artificial Intelligence of the Romanian Academy, Bucharest, Romania

    Stefan Trausan-Matu

Authors
  1. Mihaela Balint
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  2. Mihai Dascalu
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  3. Stefan Trausan-Matu
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Corresponding author

Correspondence to Mihai Dascalu .

Editor information

Editors and Affiliations

  1. Winston-Salem State University, Winston Salem, North Carolina, USA

    Christo Dichev

  2. Institute of Information and Communication Technologies, Bulgarian Academy of Sciences, Sofia, Bulgaria

    Gennady Agre

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© 2016 Springer International Publishing Switzerland

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Balint, M., Dascalu, M., Trausan-Matu, S. (2016). Classifying Written Texts Through Rhythmic Features. In: Dichev, C., Agre, G. (eds) Artificial Intelligence: Methodology, Systems, and Applications. AIMSA 2016. Lecture Notes in Computer Science(), vol 9883. Springer, Cham. https://doi.org/10.1007/978-3-319-44748-3_12

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  • DOI: https://doi.org/10.1007/978-3-319-44748-3_12

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-44747-6

  • Online ISBN: 978-3-319-44748-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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