文章基本信息

标题：A Hierarchical Feature Extraction Model for Multi-Label Mechanical Patent Classification
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作者：Hu, Jie ; Li, Shaobo ; Hu, Jianjun 等
期刊名称：Sustainability
印刷版ISSN：2071-1050
出版年度：2018
卷号：10
期号：1
页码：1-22
出版社：MDPI, Open Access Journal
摘要：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.
关键词：text feature extraction; patent analysis; hybrid neural networks; mechanical patent classification