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Bidirectional LSTM for Author Gender Identification

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Computational Collective Intelligence (ICCCI 2018)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11055))

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Abstract

Author profiling consists in inferring the authors’ gender, age, native language, dialects or personality by examining his/her written text. This important task is a very active research area because of its utility in crime, marketing and business.

In this paper, we address the problem of gender identification by applying the Long Short-Term Memory neural network architecture. Which is a novel type of recurrent network architecture that implements an appropriate gradient-based learning algorithm to overcome the vanishing-gradient problem. Experimental results show that our composition outperformed the traditional machine learning methods on gender identification.

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Notes

  1. 1.

    Wikipedia, “WikimediaDownloads.”https://dumps.wikimedia.org/arwiki/ 20170401/, 2017. [Online. Accessed 10 Apr 2017]

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Authors and Affiliations

  1. LATICE Laboratory Research, Department of Computer Science, University of Monastir, Monastir, Tunisia

    Bassem Bsir & Mounir Zrigui

Authors
  1. Bassem Bsir
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  2. Mounir Zrigui
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Corresponding authors

Correspondence to Bassem Bsir or Mounir Zrigui .

Editor information

Editors and Affiliations

  1. Faculty of Information Technology, Ton Duc Thang University, Ho Chi Minh City, Vietnam

    Ngoc Thanh Nguyen

  2. Department of Computer Science and Creative Technologies, University of the West of England, Bristol, United Kingdom

    Elias Pimenidis

  3. Department of Computer Science and Creative Technologies, University of the West of England, Bristol, United Kingdom

    Zaheer Khan

  4. Faculty of Computer Science and Management, Wrocław University of Science and Technology, Wrocław, Poland

    Bogdan Trawiński

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Bsir, B., Zrigui, M. (2018). Bidirectional LSTM for Author Gender Identification. In: Nguyen, N., Pimenidis, E., Khan, Z., Trawiński, B. (eds) Computational Collective Intelligence. ICCCI 2018. Lecture Notes in Computer Science(), vol 11055. Springer, Cham. https://doi.org/10.1007/978-3-319-98443-8_36

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  • DOI: https://doi.org/10.1007/978-3-319-98443-8_36

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-98442-1

  • Online ISBN: 978-3-319-98443-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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