Study on Chinese Named Entity Categorization based on Deep Belief Nets
Yanming Chen · Intelligent Computer and Applications · 2014
DBN is a classification of fast and global optimum neural network. It contains several layers of unsupervised networks and one layer of supervised network. The paper approves this novelty machine learning approach is suitable to the domain of named entity categorization. The paper applies RBM,an unsupervised learning method,to reconstruct more representative features from character-based features. Subsequently,the paper utilizes BP,a supervised learning method,to fine-tune parameters in whole network and accomplish the categorization task. In the end,the paper tests DBN on ACE 04 Chinese corpus and achieve 91. 45% precision,which is much better than Support Vector Machine and Back-propagation neural network.