检索规则说明:AND代表“并且”;OR代表“或者”;NOT代表“不包含”;(注意必须大写,运算符两边需空一格)
检 索 范 例 :范例一: (K=图书馆学 OR K=情报学) AND A=范并思 范例二:J=计算机应用与软件 AND (U=C++ OR U=Basic) NOT M=Visual
作 者:曹祺[1] 赵伟[1] 张英杰[1] 赵树君[2] 陈亮[1] Cao Qi;Zhao Wei;Zhang Yingjie;Zhao Shujun;Chen Liang(,2,4 Institute of Scientific and Technical Information of China, Beijing 100038;3 Wuhan University, Wuhan 430072)
机构地区:[1]中国科学技术信息研究所,北京100038 [2]武汉大学,武汉430072
出 处:《图书情报工作》2018年第13期74-81,共8页Library and Information Service
基 金:国家自然科学基金青年项目“面向专利文本中实体关系抽取的远程监督方法研究”(项目编号:71704169),国家自然科学基金青年项目“大数据挖掘在科技项目查重中的应用”(项目编号:71303223)研究成果之一
摘 要:[目的/意义]专利相似度检测(Similarity Measurement)可从宏观上辅助制定国家创新战略规划。发现国内外的热点及应对其他国家的专利流氓,从微观上为专利发明人、专利审查员、专利权人提供辅助支撑。[方法/过程]提出基于深度学习的Doc2Vec专利相似度分析方法,基于未进行清洗的专利语料库,采用深度学习的Doc2Vec模型,随机挑选了专利,研究了专利相似度检测问题,并和传统的相似度检测模型进行对比研究。[结果/结论]实验结果表明,基于深度学习的Doe2Vee模型和TF-IDF模型对于处理不做数据清洗的专利语料的结果有相近性,该方法对分析人员的专利领域知识要求较低,不需要对专利数据进行基于专利领域知识的数据清洗,同时可为专利侵权、专利查新提供新的智能工具支撑,降低研究门槛和工作量,提升研究效率。[ Purpose/significance ] Patent similarity detection assists the formulation of the national innovation strategy planning macroscopically, finds hotspots in China and all over the world, and deals with patent rogues in other countries. Microscopically, patent similarity detection provides support for patent inventors, patent examiners and patentees. [ Method/process] A new method was proposed based on deep learning of Doc2Vec model, with patent corpus based on no data clearance of domain knowledge. Then typical patents were randomly selected to carry on similarity detection by this new method, and the results with traditional similarity detection models were compared. [ Result/conclusion ] According to experimental results, the new deep learning of Doc2Vec method and TFIDF model has similary results which both of the model' s patent corpus all based on no data clearance of domain knowledge. The new method requires less professional skill in specific domain knowledge, and didn' t require the process of data clearance. It can givesa new intelligent support tool for patent infringement and patent investigation, reduce the research threshold and workload, and improve service efficiency.
正在载入数据...
正在载入数据...
正在载入数据...
正在载入数据...
正在载入数据...
正在载入数据...
正在载入数据...
正在链接到云南高校图书馆文献保障联盟下载...
云南高校图书馆联盟文献共享服务平台 版权所有©
您的IP:216.73.216.117