  We present our system for the CLIN29 shared task on cross-genre gender
detection for Dutch. We experimented with a multitude of neural models (CNN,
RNN, LSTM, etc.), more "traditional" models (SVM, RF, LogReg, etc.), different
feature sets as well as data pre-processing. The final results suggested that
using tokenized, non-lowercased data works best for most of the neural models,
while a combination of word clusters, character trigrams and word lists showed
to be most beneficial for the majority of the more "traditional" (that is,
non-neural) models, beating features used in previous tasks such as n-grams,
character n-grams, part-of-speech tags and combinations thereof. In
contradiction with the results described in previous comparable shared tasks,
our neural models performed better than our best traditional approaches with
our best feature set-up. Our final model consisted of a weighted ensemble model
combining the top 25 models. Our final model won both the in-domain gender
prediction task and the cross-genre challenge, achieving an average accuracy of
64.93% on the in-domain gender prediction task, and 56.26% on cross-genre
gender prediction.
