Convolutional Neural Networks for Question Classification in Italian language

Question Classification (QC) is a very important module, to include into the pipeline usually employed to implement the Question Answering paradigm. Recently, good results have been achieved on the QC task by using Convolutional Neural Networks (CNNs). This approach requires setting a CNN architecture and a huge number of hyperparameters to obtain the desirable achievements, and only little research has been addressed on this activity. Moreover, while the greatest part of research strength focused on English language, very few works dealt with other languages. In this work, an approach based on neural networks is used to classify Italian questions taken from a TREC dataset. In particular, different solutions regarding the CNN architecture are tested, and, according to literature advices, the best settings are searched in the proper ranges, in order to maximize the classification power for the particular case of Italian questions dataset.