Abstract
This paper discusses author profiling of English-language mails and blogs using Classification Restricted Boltzmann Machines. We propose an author profiling framework with no need for handcrafted features and only minor use of text preprocessing and feature engineering. The classifier achieves competitive results when evaluated with the PAN-AP-13 corpus: 36.59% joint accuracy, 57.83% gender accuracy and 59.17% age accuracy. We also examine the relations between discriminative, generative and hybrid training methods.
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Acknowledgments
This research was partly supported by the PL-Grid Infrastructure. The research was also supported by the AGH University of Science and Technology (AGH-UST), grant no. 11.11.230.124 (statutory project).
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Antkiewicz, M., Kuta, M., Kitowski, J. (2017). Author Profiling with Classification Restricted Boltzmann Machines. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L., Zurada, J. (eds) Artificial Intelligence and Soft Computing. ICAISC 2017. Lecture Notes in Computer Science(), vol 10245. Springer, Cham. https://doi.org/10.1007/978-3-319-59063-9_1
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DOI: https://doi.org/10.1007/978-3-319-59063-9_1
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