Exploring the Effects of Cross Genre Machine Learning for Author Profiling in PAN 2016
Author profiling deals with the study of various profile dimensions of an author such as age and gender. This work describes our methodology proposed for the task of cross genre author profiling at PAN 2016. We address gender and age prediction as a classification task and approach this problem by extracting stylistic and lexical features for training a logistic regression model. Furthermore, we report the effects of our cross genre machine learning approach for the author profiling task. With our approach, we achieved the first place for gender detection in English and tied for second place in terms of joint accuracy. For Spanish, we tied for first place.
CLEF