《计算机应用研究》|Application Research of Computers

基于双通道特征融合的WPOS-GRU专利分类方法

WPOS-GRU patent classification method based on two-channel feature fusion

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作者 余本功,张培行
机构 合肥工业大学 a.管理学院;b.过程优化与智能决策教育部重点实验室,合肥 230009
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文章编号 1001-3695(2020)03-003-0655-04
DOI 10.19734/j.issn.1001-3695.2018.08.0628
摘要 为提高专利文本自动分类的效率和准确度,提出一种基于双通道特征融合的WPOS-GRU(word2vec and part of speech gated recurrent unit)专利文本自动分类方法。首先获取专利摘要文本,并进行清洗和预处理;然后对专利文本进行词向量表示和词性标注,并将专利文本分别映射为word2vec词向量序列和POS词性序列;最后使用两种特征通道训练WPOS-GRU模型,并对模型效果进行实验分析。通过对比传统专利分类方法和单通道专利分类方法,双通道特征融合的WPOS-GRU专利分类方法提高了分类效果。提出的方法节省了大量的人力成本,提高了专利文本分类的准确度,更能满足大量专利文本分类任务自动化高效率的需要。
关键词 专利分类; 词性标注; 特征融合; 门限递归单元
基金项目 国家自然科学基金资助项目(71671057)
本文URL http://www.arocmag.com/article/01-2020-03-003.html
英文标题 WPOS-GRU patent classification method based on two-channel feature fusion
作者英文名 Yu Bengong, Zhang Peihang
机构英文名 a.School of Management,b.Key Laboratory of Process Optimization & Intelligent Decision-Making of Ministry of Education,Hefei University of Technology,Hefei 230009,China
英文摘要 In order to improve the efficiency and accuracy of patent text automatic classification, this paper proposed a WPOS-GRU patent text automatic classification method based on two-channel feature fusion. Firstly, this method obtained, cleaned and pretreated the patent summary text, then represented the patent text by word vector and part-of-speech tagging, and mapped the patent text into word2vec word vector sequence and POS part-of-speech sequence respectively. Finally, this paper trained WPOS-GRU model by two feature channels, and analyzed experimentally the effect of the model. By comparing the traditional patent classification method with the single-channel patent classification method, the WPOS-GRU patent classification method based on two-channel feature fusion improves the classification effect. The proposed method saves a lot of manpower costs, improves the accuracy of patent text classification, and can meet the needs of automation and high efficiency of a large number of patent text classification tasks.
英文关键词 patent classification; part of speech tagging; feature fusion; GRU
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收稿日期 2018/8/25
修回日期 2018/10/24
页码 655-658
中图分类号 TP391
文献标志码 A