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We present our system for the CLIN29 shared task on cross-genre genderdetection for Dutch. We experimented with a multitude of neural models (CNN,RNN, LSTM, etc.), more "traditional" models (SVM, RF, LogReg, etc.), differentfeature sets as well as data pre-processing. The final results suggested thatusing tokenized, non-lowercased data works best for most of the neural models,while a combination of word clusters, character trigrams and word lists showedto 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. Incontradiction with the results described in previous comparable shared tasks,our neural models performed better than our best traditional approaches withour best feature set-up. Our final model consisted of a weighted ensemble modelcombining the top 25 models. Our final model won both the in-domain genderprediction task and the cross-genre challenge, achieving an average accuracy of64.93% on the in-domain gender prediction task, and 56.26% on cross-genregender prediction.

ABI Neural Ensemble Model for Gender Prediction Adapt Bar-Ilan Submission for the CLIN29 Shared Task on Gender Prediction