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International Conference of the Cross-Language Evaluation Forum for European Languages

CLEF 2017: Experimental IR Meets Multilinguality, Multimodality, and Interaction pp 119–124Cite as

Estimating the Similarities Between Texts of Right-Handed and Left-Handed Males and Females

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10456))

Abstract

Identifying the characteristics of text authors is of critical importance for marketing, security, etc., and there has been a growing interest in this issue recently. A major feature to be researched using text analysis has been gender. Despite a lot of studies that have obviously contributed to the progress in the field, identification of gender with text authors so far remains challenging and daunting. One of the reasons is that current research shows no consideration of the mutual influences of various individual characteristics including gender and laterality. In this paper, using the material of a specially designed corpus of Russian texts named RusNeuroPsych, including the neuropsychological data of the authors, we calculated the distance between texts written by right-handed and left-handed males and females (4 classes). For this study we have chosen handedness as one of the most important laterality measures. In order to calculate the distance between the classes, a formula measuring the Wave-Hedges distance was employed. The text parameters were topic-independent and frequent (the indices of lexical diversity, a variety of parts of speech ratios, etc.). It was shown that texts by authors of different genders but with an identical type of handedness are more similar linguistically than those by individuals of the same gender but with a different type of manual preference. We suppose that it could be useful to build a classifier for classes “gender + handedness” instead of predicting gender itself.

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Notes

  1. 1.

    The corpus is freely available at http://en.rusprofilinglab.ru/korpus-tekstov/rusneuropsych-corpus/.

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Acknowledgments

This research is financially supported by the Russian Science Foundation, project No. 16-18-10050, “Identifying the Gender and Age of Online Chatters Using Formal Parameters of their Texts”.

Author information

Authors and Affiliations

  1. RusProfiling Lab, Voronezh, 394043, Russia

    Tatiana Litvinova, Pavel Seredin, Olga Litvinova & Ekaterina Ryzhkova

  2. Voronezh State University, Voronezh, 394018, Russia

    Pavel Seredin

  3. The Kurchatov Institute, Moscow, 123098, Russia

    Tatiana Litvinova & Pavel Seredin

Authors
  1. Tatiana Litvinova
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  2. Pavel Seredin
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  3. Olga Litvinova
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  4. Ekaterina Ryzhkova
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Corresponding author

Correspondence to Tatiana Litvinova .

Editor information

Editors and Affiliations

  1. Dublin City University, Dublin, Ireland

    Gareth J.F. Jones

  2. Trinity College Dublin, Dublin, Ireland

    Séamus Lawless

  3. National University of Distance Education, Madrid, Spain

    Julio Gonzalo

  4. Dublin City University, Dublin, Ireland

    Liadh Kelly

  5. Université Grenoble Alpes, Grenoble, France

    Lorraine Goeuriot

  6. University of Hildesheim, Hildesheim, Germany

    Thomas Mandl

  7. University of Padua, Padua, Italy

    Linda Cappellato

  8. University of Padua, Padua, Italy

    Nicola Ferro

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Litvinova, T., Seredin, P., Litvinova, O., Ryzhkova, E. (2017). Estimating the Similarities Between Texts of Right-Handed and Left-Handed Males and Females. In: Jones, G., et al. Experimental IR Meets Multilinguality, Multimodality, and Interaction. CLEF 2017. Lecture Notes in Computer Science(), vol 10456. Springer, Cham. https://doi.org/10.1007/978-3-319-65813-1_11

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  • DOI: https://doi.org/10.1007/978-3-319-65813-1_11

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-65812-4

  • Online ISBN: 978-3-319-65813-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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