关键词: 深度学习, 海军军械, 不均衡数据集, 双向循环神经网络, 文本数据挖掘

Abstract: Traditional unbalanced data sets processing methods are characterized by complicated artificial settings and poor universality, which are difficult to be applied to naval ordnance unbalanced text data sets processing. Aiming at this problem, this paper proposes a method of processing unbalanced text data sets of naval ordnance based on biRNN model. The biRNN model is used to automatically learn the features of text sequences and expand a few types of texts by two-way text sequence prediction to achieve the goal of text data balancing. The whole text data set is expanded on the basis of balanced data set. Text classification experiments are carried out on the original data set, the balanced data set and the extended data set. The experimental results show that the unbalanced data set expansion method based on biRNN can effectively improve the performance of text classification by balancing and extending the original data set.

Key words: deep learning, naval ordnance, unbalanced data set, bidirectional recurrent neural network, text data mining