PANcakes team: A composite system of genre-Agnostic features for author profiling

Pepa Gencheva, Martin Boyanov, Elena Deneva, Preslav Nakov, Yasen Kiprov, Ivan Koychev, Georgi Georgiev

Research output: Contribution to journalConference article

5 Citations (Scopus)

Abstract

We present the system we built for participating in the PAN-2016 Author Profiling Task [9]. 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.

Original languageEnglish
Pages (from-to)874-880
Number of pages7
JournalCEUR Workshop Proceedings
Volume1609
Publication statusPublished - 1 Jan 2016

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ASJC Scopus subject areas

  • Computer Science(all)

Cite this

Gencheva, P., Boyanov, M., Deneva, E., Nakov, P., Kiprov, Y., Koychev, I., & Georgiev, G. (2016). PANcakes team: A composite system of genre-Agnostic features for author profiling. CEUR Workshop Proceedings, 1609, 874-880.