Including Dialects and Language Varieties in Author Profiling

2017-07-03 · Alina Maria Ciobanu, Marcos Zampieri, Shervin Malmasi, Liviu P. Dinu

This paper presents a computational approach to author profiling taking gender and language variety into account. We apply an ensemble system with the output of multiple linear SVM classifiers trained on character and word $n$-grams. We evaluate the system using the dataset provided by the organizers of the 2017 PAN lab on author profiling. Our approach achieved 75% average accuracy on gender identification on tweets written in four languages and 97% accuracy on language variety identification for Portuguese.

📄 PDF Abstract BibTeX arXiv:1707.00621

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Author Profiling

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