DOI: 10.14704/nq.2018.16.6.1555

Packaging Domain-Based Named Entity Recognition with Multi-Layer Neural Networks

Changyun Li, Yuezhong Wu, Fanghuai Hu, Changsheng Liu

Abstract


Artificial neural networks (ANNs) are the greatest success story that inspired by biological neural networks and neuroscience; ANNs model realistic problems by a network of neurons which are designed by simulating biological neurons. This paper attempts to design a multi-layer neural network to recognize the named entities in packaging domain. For this purpose, a neural network language model was designed to automatically learn the distributed word features and partial speech features. Based on these distributed features, a multi-layer deep neural network model was constructed for the NER of packaging products. The experiments prove that our model can automatically extract more and better advanced features than traditional methods, thus minimizing the workloads of manual feature selection. The results show that the model outperformed the traditional sequence labelled CRF model by 10% in precision and 6% in recall, and that the four-layer neural network with two hidden layers boasted the best NER of packaging products.

Keywords


Packaging, Named Entity Recognition (NER), Neural Network, Computational Neuroscience

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