Text Retrieval analysis based on Deep Learning
Kai Liu, Limin Zhang, Yongwei Sun · Advances in computer science research · 2015
In view of the advantages of deep learning model in the extraction of abstract concept, a new text clustering algorithm is designed based on Deep Boltzmann Machines.Based on Replicate Softmax Model and new Deep Boltzmann Machine, energy function of this model is proposed and the detail learning algorithm is introduced.The learning can be made more efficient by using a layer-by-layer "pre-training" phase that allows variation inference to be initialized with a single bottom up pass.The values of the latent variables in the deepest layer are easy to infer and give a much better representation of each document than low learning.The 20-newsgroups document sets experiment results illustrated that the novel algorithm learn good generative models, get the better competence of a shallow model-Replicate Softmax Model in handling with an extract abstract concept and has good feasibility in large scale text clustering analysis.