| Resumen |
We present the CIC’s approach to the Author Profiling (AP) task at PAN 2017. This year task consists of two subtasks: gender and language variety identification in English, Spanish, Portuguese, and Arabic. We use typed and untyped character n-grams, word n-grams, and non-textual features (domain names). We experimented with various feature representations (binary, raw frequency, normalized frequency, log-entropy weighting, tf-idf), machine-learning algorithms (liblinear and libSVM implementations of Support Vector Machines (SVM), multinomial naive Bayes, ensemble classifier, meta-classifiers), and frequency threshold values. We adjusted system configurations for each of the languages and subtasks. |
