Gender Prediction from Tweets: Improving Neural Representations with Hand-Crafted Features
Table 3: Accuracy on PAN 2018 test set.
['Model', 'English', 'Spanish', 'Arabic']
These results show that an attention layer is able to learn ”where/how to look” for features that are helpful in identifying the gender of a user. On the other hand, the improved model (RNNwA + n-gram), where neural and hand-crafted features are concatenated, increases the accuracy of the proposed model by approximately 0,5% on English and approximately 2% in Spanish and Arabic. This also supports our intuition that the performance of neural models can be improved by hand-crafted features, which is based on the study of Collobert and Weston