Predicting gender by the name is not a simple task. In many applications, especially in the natural language processing (NLP) field, this task may be necessary, mainly when considering foreign names. Some machine learning algorithms can satisfactorily perform the prediction. In this paper, we examined and implemented feedforward and recurrent deep neural network models, such as MLP, RNN, GRU, CNN, and BiLSTM, to classify gender through the first name. A dataset of Brazilian names is used to train and evaluate the models. We analyzed the accuracy, recall, precision, and confusion matrix to measure the models' performances. The results indicate that the gender prediction can be performed from the feature extraction strategy looking at the names as a set of strings. Some models accurately predict the gender in more than 90% of the cases. The recurrent models overcome the feedforward models in this binary classification problem.
翻译:以姓名预测性别并不是一项简单的任务。 在许多应用中,特别是在自然语言处理( NLP) 领域,这项任务可能是必要的, 主要是在考虑外国名称时。 一些机器学习算法可以令人满意地进行预测。 在本文中, 我们检查并实施了反馈和反复出现的深神经网络模型, 如 MLP、 RNN、 GRU、 CNN 和 BILSTM, 以通过名字对性别进行分类。 使用巴西地名数据集来训练和评价模型。 我们分析了精确性、 回溯性、 精确性和 混乱矩阵, 以衡量模型的性能。 结果表明, 性别预测可以从特征提取战略中进行, 将名称作为一组字符进行。 一些模型精确地预测了90%以上案例的性别。 经常模型克服了二元分类问题的进化模型。