Hierarchical Attention-based BiLSTM Network for Document Similarity Calculation

Abstract

Neural network model is a momentous method to calculate semantic similarity. Taking into account the complexity of document structure, introducing hierarchical structure and attention mechanism into neural network can calculate document semantic representation more precisely. In order to verify the validity of the model, LP50 dataset was tested. The experimental results reveal that accurate document representation can be obtained by using the attention mechanism at two levels of words and sentences. Since this method has taken both the influence of context information and the contribution of components to the document into consideration. Compared with several conventional methods, there is a significant improvement of performance in our model.