We present the system we built for participating in the PAN-2016 Author Profiling Task . The task asked to predict the gender and the age group of a person given several samples of his/her writing, and it was offered for three different languages: English, Spanish, and Dutch. We participated in both subtasks, for all three languages. Our approach focused on extracting genre-Agnostic features such as bag-of-words, sentiment and topic derivation, and stylistic features. We then used these features to train SVM-based classifiers, as implemented in LIBLINEAR for the gender classification sub-Task, and in LIBSVM for the age classification sub-Task.
|Number of pages||7|
|Journal||CEUR Workshop Proceedings|
|Publication status||Published - 1 Jan 2016|
ASJC Scopus subject areas
- Computer Science(all)