Cross domain author profiling task in spanish language: an experimental study


  • María José Garciarena Ucelay LIDIC (Research Group), Universidad Nacional de San Luis, San Luis, Argentina
  • María Paula Villegas LIDIC (Research Group), Universidad Nacional de San Luis, San Luis, Argentina
  • Leticia Cagnina LIDIC (Research Group), Universidad Nacional de San Luis, San Luis, Argentina
  • Marcelo Luis Errecalde LIDIC (Research Group), Universidad Nacional de San Luis, San Luis, Argentina


Author Profiling, Natural Processing Language, Cross Domain Classification, Spanish Language, Text Mining


Author Profiling is the task of predicting characteristics of the author of a text, such as age, gender, personality, native language, etc. This is a task of growing importance due to the potential applications in security, crime detection and marketing, among others. An interesting point is to study the robustness of a classifier when it is trained with a data set and tested with others containing different characteristics. Commonly this is called cross domain experimentation. Although different cross domain studies have been done for data sets in English language, for Spanish it has recently begun. In this context, this work presents a study of cross domain classification for the author profiling task in Spanish. The experimental results showed that using corpora with different levels of formality we can obtain robust classifiers for the author profiling task in Spanish language.


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How to Cite

Garciarena Ucelay, M. J., Villegas, M. P., Cagnina, L., & Errecalde, M. L. (2015). Cross domain author profiling task in spanish language: an experimental study. Journal of Computer Science and Technology, 15(02), p. 122–128. Retrieved from



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