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Document Model with Attention Bidirectional Recurrent Network for Gender Identification

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Advances in Computational Intelligence (IWANN 2019)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11506))

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Abstract

Author profiling is an important statistical and semantic processing task Author profiling is an important statistical and semantic processing task in the field of natural language processing (NLP). It refers to the extraction of information from author’s texts such as gender, age and other kinds of personality traits. Author profiling can be applied in various fields like marketing, security and forensics. In this work, we explore how bi-directional deep learning architectures can be used to learn the abstract and higher-level features of the document, which could be employed to identify the author’s gender. To deal with this, we extend Bidirectional Long Short-Term Memory Networks Language Models with an attention mechanism. The originality of our approach lays in its ability to capture the most important semantic information in a sentence. The experimental results on Facebook and twitter corpus show that our method outperformed the majority of the existing methods.

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Notes

  1. 1.

    https://techcrunch.com/2017/06/27/facebook-2-billion-users/.

  2. 2.

    Wikipedia, “WikimediaDownloads.” https://dumps.wikimedia.org/arwiki/20170401/, 2017. [Online; accessed 10-April-2017].

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Author information

Authors and Affiliations

  1. Research Laboratory in Algebra, Numbers Theory and Intelligent Systems, 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. University of Granada, Granada, Spain

    Ignacio Rojas

  2. University of Malaga, Malaga, Spain

    Gonzalo Joya

  3. Polytechnic University of Catalonia, Barcelona, Spain

    Andreu Catala

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Bsir, B., Zrigui, M. (2019). Document Model with Attention Bidirectional Recurrent Network for Gender Identification. In: Rojas, I., Joya, G., Catala, A. (eds) Advances in Computational Intelligence. IWANN 2019. Lecture Notes in Computer Science(), vol 11506. Springer, Cham. https://doi.org/10.1007/978-3-030-20521-8_51

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  • DOI: https://doi.org/10.1007/978-3-030-20521-8_51

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-20520-1

  • Online ISBN: 978-3-030-20521-8

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