Multilingual Author Profiling using LSTMs: Notebook for PAN at CLEF 2018

Roy Khristopher Bayot, Teresa Gonçalves
2018 Conference and Labs of the Evaluation Forum  
This paper shows one approach of the Universidade de Évora for author profiling for PAN 2018. The approach mainly consists of using word vectors and LSTMs for gender classification. Using the PAN 2018 dataset, we achieved an accuracy of 67.60% for Arabic, 77.16% for English, and 68.73% for Spanish gender classification.
dblp:conf/clef/BayotG18 fatcat:7gjohpuljvbbjnfc75vuj7f5u4