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Article

A Hierarchical Feature Extraction Model for Multi-Label Mechanical Patent Classification

by 1, 1,2,*, 2,3 and 1
1
Key Laboratory of Advanced Manufacturing Technology of Ministry of Education, Guizhou University, Guiyang 550025, China
2
School of Mechanical Engineering, Guizhou University, Guiyang 550025, China
3
Department of Computer Science and Engineering, University of South Carolina, Columbia, SC 29208, USA
*
Author to whom correspondence should be addressed.
Sustainability 2018, 10(1), 219; https://doi.org/10.3390/su10010219
Received: 30 November 2017 / Revised: 13 January 2018 / Accepted: 15 January 2018 / Published: 16 January 2018
Various studies have focused on feature extraction methods for automatic patent classification in recent years. However, most of these approaches are based on the knowledge from experts in related domains. Here we propose a hierarchical feature extraction model (HFEM) for multi-label mechanical patent classification, which is able to capture both local features of phrases as well as global and temporal semantics. First, a n-gram feature extractor based on convolutional neural networks (CNNs) is designed to extract salient local lexical-level features. Next, a long dependency feature extraction model based on the bidirectional long–short-term memory (BiLSTM) neural network model is proposed to capture sequential correlations from higher-level sequence representations. Then the HFEM algorithm and its hierarchical feature extraction architecture are detailed. We establish the training, validation and test datasets, containing 72,532, 18,133, and 2679 mechanical patent documents, respectively, and then check the performance of HFEMs. Finally, we compared the results of the proposed HFEM and three other single neural network models, namely CNN, long–short-term memory (LSTM), and BiLSTM. The experimental results indicate that our proposed HFEM outperforms the other compared models in both precision and recall. View Full-Text
Keywords: text feature extraction; patent analysis; hybrid neural networks; mechanical patent classification text feature extraction; patent analysis; hybrid neural networks; mechanical patent classification
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MDPI and ACS Style

Hu, J.; Li, S.; Hu, J.; Yang, G. A Hierarchical Feature Extraction Model for Multi-Label Mechanical Patent Classification. Sustainability 2018, 10, 219. https://doi.org/10.3390/su10010219

AMA Style

Hu J, Li S, Hu J, Yang G. A Hierarchical Feature Extraction Model for Multi-Label Mechanical Patent Classification. Sustainability. 2018; 10(1):219. https://doi.org/10.3390/su10010219

Chicago/Turabian Style

Hu, Jie, Shaobo Li, Jianjun Hu, and Guanci Yang. 2018. "A Hierarchical Feature Extraction Model for Multi-Label Mechanical Patent Classification" Sustainability 10, no. 1: 219. https://doi.org/10.3390/su10010219

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