Abstract: Aimed at entity recognition in the field of nonferrous metal, and oriented the complex and strongly nested structure characteristics of domain entity such as product names, organizations, and placenames, it is proposed a kind of nonferrous metal industry entity recognition model based on deep neural network (DNN) architecture. In order to effectively use the characteristics of tight combination between the characters of domain entity and to bypass the Chinese words segmentation in the professional field, the model uses neural networks to automatically learn the word embeddings vector representation of Chinese characteristics as its inputs. The denoising autoencoder (DAE) of text window makes some pre-training on each DNN hidden layer. The pre-training extracts optimal feature vector combination which will be used in nonferrous metal domain entity recognition. Moreover, we detailedly describe the pre-training process that the denoising autoencoder of text window based on neuron language model makes and the construction process of deep network on nonferrous metal entity recognition. Finally, to validate the method’s effectiveness, we make some experiments of entity recognition on several entity types, such as product names, mineral names and place names in nonferrous metal domain. The experimental results show that the proposed model has good effect on the entity recognition of the professional domain.

Key words: field of nonferrous metal, deep neural network(DNN), word embeddings, denoising autoencoder(DAE), entity recognition