Classifying nouns in Portuguese into gender categories: a deep learning approach
Natalia Resende and Raquel Chaves

In Portuguese, all nouns are distributed into two gender categories: feminine and masculine. On one hand, gender can be predicted from the phonological cues present in the endings of the nouns. For example, nouns ending in -a tend to be feminine and nouns ending in -o tend to be masculine. On the other hand, the relationship between word ending and gender is far from being a consistent rule, since nouns ending in other phonemes may be of either gender. In the present study, a connectionist network was trained to classify Portuguese nouns into gender categories considering their phonological structure as a whole. The performance of the network was analysed in detail to check whether the network considers only the endings of the nouns or their whole phonological structure for gender decisions. In addition, it was analysed what type of information the network takes into account to decide the gender of nouns whose endings are not predictive of gender. Results show an error-free performance when the network takes into account the phonological information present in the endings of the nouns and frequency effects for nonpredictive endings. The present study has implications to the training of NLP systems when classifying nouns into gender categories.