摘要
Abstract
In order to solve the problem of country topic indexing of massive literature, and to explore the use of deep learning in the field of knowledge organization under the background of "Internet + Big Data", this paper proposes a country topic indexing method based on deep convolutional neural network. On the basis of exploring the feasibility of converting the country topic indexing task into a multi-label classification task, this method use the natural language processing method to vectorize the full text of the document as the first step, and then use pre-trained word embedding to transform the document vector into a tensor rich in semantic relationships between words. Thirdly, using deep convolution neural networks to automatically extract text features from vocabulary, sentences, paragraphs, and chapters layer by layer, generates a volume rich in full-text semantics. Finally, the probability of the country label being output by the full connection layer. The experimental results show that the method achieves the desired effect, has a high accurate classification performance and good generalization ability, and provides a valuable reference for the application of deep learning algorithm in the field of knowledge organization.关键词
知识组织/主题标引/深度学习/深度卷积神经网络Key words
Knowledge Organization/Subject Indexing/Deep Learning/Deep Convolution Neural Networks分类
社会科学
