Improving Question Classification with Hybrid Networks

Question classification is a basic work in natural language processing, which has an important influence on question answering. Due to question sentences are complicated in many specific domains contain a large number of exclusive vocabulary, question classification becomes more difficult in these fields. To address the specific challenge, in this paper, we propose a novel hierarchical hybrid deep network for question classification. Specifically, we first take advantages of word2vec and a synonym dictionary to learn the distributed representations of words. Then, we exploit bi-directional long short-term memory networks to obtain the latent semantic representations of question sentences. Finally, we utilize convolutional neural networks to extract question sentence features and obtain the classification results by a fully-connected network. Besides, at the beginning of the model, we leverage the self-attention layer to capture more useful features between words, such as potential relationships, etc. Experimental results show that our model outperforms common classifiers such as SVM and CNN. Our approach achieves up to 9.37% average accuracy improvements over baseline method across our agricultural dataset.