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


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.


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

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Cannistraro M, Lorenzini E. The applications of the new technologies “e-sensing” in hospitals. International Journal of Heat and Technology 2016; 34(4): 551-57.

Chen J. Survey of named entity recognition. Modern Computer 2016; 3: 24-26.

David DC, Thomas D. Neural networks and neuroscience-inspired computer vision. Current Biology 2014; 24(18): 921-29.

Ge WM. Internet artificial intelligence: seize the opportunity to seize the top of the industrial revolution. World Telecommunications 2015; 8: 2-74.

Gonçalves CP. Quantum neural machine learning: backpropagation and dynamics. NeuroQuantology 2017; 15(1): 22-41.

Hu C, Ding C, Dai L. Modeling and development of medical information system based on support vector machine in web network. International Journal Bioautomation 2017; 21(S4): 283-292

Huai BX, Bao TF, Zhu HS. A named entity linking method based on probability topic model. Journal of Software 2014; 9: 2076-87.

Lakshmi G, Panicker JR, Meera M. Named entity recognition in Malayalam using fuzzy support vector machine. International Conference on Information Science 2017: 201-06.

Liu FF, Zhao J, Lv BB. Research on product named entity recognition for business information extraction. Journal of Chinese Information Processing 2006; 20(1): 7-13.

Mahalakshmi GS, Betina AJ, Bagawathi RS. domain based named entity recognition using naive bayes classification. Social Science Electronic Publishing 2016; 10(102): 234-39.

Mei F, Sun C.J, Sun K. Chinese product named entity recognition based on network text. Journal of Zhengzhou University: Natural Science Ed 2010; 42(1): 62-66.

Putthividhya D, Hu J. Bootstrapped named entity recognition for product attribute extraction. Conference on Empirical Methods in Natural Language Processing 2011; 1557-67.

Wang HL. Named entity recognition of Chinese microblog product based on word-vector clustering. Journal of Lanzhou University of Technology 2017; 43(1): 104-10.

Wu S, Fang Z, Tang J. Accurate Product Name Recognition from user generated content. International Conference on Data Mining Workshops 2013: 874-77.

Zhang CS, Guo JY, Yan XT. Named entity recognition of English products based on conditional random field. Computer Engineering and Science 2010, 32(6): 115-17.

Zheng QS, Liu SX. Research of web text named entity recognition based on CRF. Journal of Zhongyuan University of Technology 2016; 27(1): 70-73, 95.

Zhong ZN, Liu FC, Wu Y. Active learning and self-learning Chinese named entity recognition. Journal of National University of Defense Technology 2014; 4: 82-88.

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