基于辅助集的专利主题分析领域停用词选取
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摘要
[目的]提出一种领域停用词自动选取方法,以提高专利主题分析的区分度和质量。[方法]针对要进行专利主题分析的目标集,引入专利辅助集,提出基于辅助集文档频率和类别熵两个指标,衡量词语在辅助集中分布情况,自动识别领域停用词。[结果]实验结果表明,基于辅助集的领域停用词选取方法能够提高专利主题分析的区分度和质量。[局限]辅助集的选取类型和数量有待进一步研究。[结论]基于辅助集的领域停用词选取方法能够有效地衡量词的分布特征,从而更准确地选取专利主题分析中的领域停用词。
[Objective] This paper proposes a new method to automatically choose domain specific stopwords, aiming to improve the performance of patent topic analysis. [Methods] First, we introduced an auxiliary set and proposed two indexes of document frequency and entropies among categories based on this auxiliary set. Then, we measured the distribution of words from the auxiliary set to choose the domain specific stopwords automatically. [Results] The proposed method improved the quality of identified patent topics. [Limitations] The types and members of the auxiliary set need to be further studied. [Conclusions] The proposed stopwords selection methods could measure the characteristics of words, which helps us find the domain specific stopwords for patent analysis more effectively.
[Objective] This paper proposes a new method to automatically choose domain specific stopwords, aiming to improve the performance of patent topic analysis. [Methods] First, we introduced an auxiliary set and proposed two indexes of document frequency and entropies among categories based on this auxiliary set. Then, we measured the distribution of words from the auxiliary set to choose the domain specific stopwords automatically. [Results] The proposed method improved the quality of identified patent topics. [Limitations] The types and members of the auxiliary set need to be further studied. [Conclusions] The proposed stopwords selection methods could measure the characteristics of words, which helps us find the domain specific stopwords for patent analysis more effectively.
引文
[1] Tang J,Wang B,Yang Y,et al. PatentMiner:Topic-driven Patent Analysis and Mining[C]//Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Beijing, China. New York:ACM Press,2012:1366-1374.
[2] Wang B,Liu S,Ding K,et al. Identifying Technological Topics and Institution-topic Distribution Probability for Patent Competitive Intelligence Analysis:A Case Study in LTE Technology[J]. Scientometrics, 2014, 101(1):685-704.
[3] Chen H,Zhang G,Lu J,et al. A Fuzzy Approach for Measuring Development of Topics in Patents Using Latent Dirichlet Allocation[C]//Proceedings of IEEE International Conference on Fuzzy Systems, Istanbul, Turkey. Washington DC:IEEE Computer Society, 2015.
[4] Kim M, Park Y, Yoon J. Generating Patent Development Maps for Technology Monitoring Using Semantic Patent-topic Analysis[J]. Computers&Industrial Engineering, 2016, 98(3):289-299.
[5] Suominen A,Toivanen H,Seppanen M. Firms'KnowledgeProfiles:Mapping Patent Data with Unsupervised Learning[J]. Technological Forecasting&Social Change,2016, 115:131-142.
[6]范宇,符红光,文奕.基于LDA模型的专利信息聚类技术[J].计算机应用,2013,33(1):87-89.(Fan Yu,Fu Hongguang, Wen Yi. Patent Information Clustering Technique Based on Latent Dirichlet Allocation Model[J].Journal of Computer Applications, 2013, 33(1):87-89.)
[7]王博,刘盛博,丁堃,等.基于LDA主题模型的专利内容分析方法[J].科研管理,2015, 36(3):111-117.(Wang Bo, Liu Shengbo, Ding Kun, et al. Patent Content Analysis Method Based on LDA Topic Model[J]. Science Research Management, 2015, 36(3):111-117.)
[8]吴菲菲,张亚茹,黄鲁成,等.基于AToT模型的技术主题多维动态演化分析——以石墨烯技术为例[J].图书情报工作,2017,61(5):95-102.(Wu Feifei, Zhang Yarn, Huang Lucheng, et al. Multi-dimension Dynamic Evolution Analysis of Technology Topics Based on AToT by Taking Grapheme Technology as an Example[J]. Library and Information Service, 2017, 61(5):95-102.)
[9]廖列法,勒孚刚.基于LDA模型和分类号的专利技术演化研究[J].现代情报,2017,37(5):13-18.(Liao Liefa,Le Fugang. Research on Patent Technology Evolution Based on LDA Model and Classification Number[J]. Modern Information, 2017, 37(5):13-18.)
[10]陈亮,张静,张海超,等.层次主题模型在技术演化分析上的应用研究[J].图书情报工作,2017, 61(5):103-108.(Chen Liang, Zhang Jing, Zhang Haichao, et al. Application of Hierarchical Topic Model on Technological Evolution Analysis[J]. Library and Information Service, 2017, 61(5):103-108.)
[11] Frakes W B, Baeza-Yates R. Information Retrieval:Data Structures and Algorithms[M]. Prentice-Hall, 1992.
[12] Silva C, Ribeiro B. The Importance of Stop Word Removal on Recall Values in Text Categorization[C]//Proceedings of International Joint Conference on Neural Networks,Portland. Washington DC:IEEE Computer Society, 2003:1661-1666.
[13]官琴,邓三鸿,王昊.中文文本聚类常用停用词表对比研究[J].数据分析与知识发现,2017, 1(3):72-80.(Guan Qin,Deng Sanhong,Wang Hao. Chinese Stopwords for Text Clustering:A Comparative Study[J]. Data Analysis and Knowledge Discovery, 2017, 1(3):72-80.)
[14] Crow D, Desanto J. A Hybrid Approach to Concept Extraction and Recognition-based Matching in the Domain of HumanResources[C]//Proceedings of IEEE International Conference on TOOLS with Artificial Intelligence, Boca Raton, USA.Washington DC:IEEE Computer Society, 2004:535-541.
[15] Seki K,Mostafa J. An Application of Text Categorization Methods to Gene Ontology Annotation[C]//Proceedings of the 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval,Salvador, Brazil. New York:ACM Press, 2005:138-145.
[16] Tong S,Lerner U, Singhal A, et al. Locating Meaningful Stopwords or Stop-phrases in Keyword-based Retrieval Systems:US:9817920[P/OL].[2012-07-03].[2017-11-14].https://patents.google.com/patent/US7409383B1/en.
[17] White B J. Impact of Domain-specific Stop-word Lists on ECommerce Website Search Performance[J]. Journal of Strategic E-Commerce, 2007, 5(2):83-102.
[18] Lo T W, He B, Ounis I. Automatically Building a Stopword List for an Information Retrieval System[J]. Journal of Digital Information Management, 2005, 3(1):3-8.
[19] Hao L, Hao L. Automatic Identification of Stop Words in Chinese Text Classification[C]//Proceedings of International Conference on Computer Science and Software Engineering.Washington DC:IEEE Computer Society, 2008:718-722.
[20] Sinka M P, Corne D W. Evolving Better Stoplists for Document Clustering and Web Intelligence[C]//Proceedings of the 3rd International Conference on Hybrid Intelligent Systems, Melbourne, Australia. Amsterdam:IOS Press, 2008:1015-1023.
[21] Jungiewicz M,Lopuszynski M. Unsupervised Keyword Extraction from Polish Legal Texts[C]//Proceedings of the International Conference on Natural Language Processing,Warsaw,Poland. New York:Springer Publishing Company,2014:65-70.
[22] Makrehchi M,Kamel M S. Extracting Domain-specific Stopwords for Text Classifiers[J]. Intelligent Data Analysis,2017,21(1):39-62.
[23]顾益军,樊孝忠,王建华,等.中文停用词表的自动选取[J].北京理工大学学报,2005, 25(4):337-340.(Gu Yijun,Fan Xiaozhong, Wang Jianhua, et al. Automatic Selection of Chinese Stoplist[J]. Transactions of Beijing Institute of Technology, 2005, 25(4):337-340.)
[24]巩政,关高娃.蒙古文停用词和英文停用词比较研究[J].中文信息学报,2011,25(4):35-38.(Gong Zheng,Guan Gaowa. A Comparative Study on Between Mongolian Stop Words and English Stop Words[J]. Journal of ChineseInformation Processing, 2011, 25(4):35-38.)
[25]珠杰,李天瑞.藏文停用词选取与自动处理方法研究[J].中文信息学报,2015, 29(2):125-132.(Zhu Jie,Li Tianrui.Research on Tibetan Stop Words Selection and Automatic Processing Method[J]. Journal of Chinese Information Processing, 2015, 29(2):125-132.)
[26] Blei D M, Ng A Y, Jordan M I. Latent Dirichlet Allocation[J].Journal of Machine Learning Research, 2003, 3(1):993-1022.
[2] Wang B,Liu S,Ding K,et al. Identifying Technological Topics and Institution-topic Distribution Probability for Patent Competitive Intelligence Analysis:A Case Study in LTE Technology[J]. Scientometrics, 2014, 101(1):685-704.
[3] Chen H,Zhang G,Lu J,et al. A Fuzzy Approach for Measuring Development of Topics in Patents Using Latent Dirichlet Allocation[C]//Proceedings of IEEE International Conference on Fuzzy Systems, Istanbul, Turkey. Washington DC:IEEE Computer Society, 2015.
[4] Kim M, Park Y, Yoon J. Generating Patent Development Maps for Technology Monitoring Using Semantic Patent-topic Analysis[J]. Computers&Industrial Engineering, 2016, 98(3):289-299.
[5] Suominen A,Toivanen H,Seppanen M. Firms'KnowledgeProfiles:Mapping Patent Data with Unsupervised Learning[J]. Technological Forecasting&Social Change,2016, 115:131-142.
[6]范宇,符红光,文奕.基于LDA模型的专利信息聚类技术[J].计算机应用,2013,33(1):87-89.(Fan Yu,Fu Hongguang, Wen Yi. Patent Information Clustering Technique Based on Latent Dirichlet Allocation Model[J].Journal of Computer Applications, 2013, 33(1):87-89.)
[7]王博,刘盛博,丁堃,等.基于LDA主题模型的专利内容分析方法[J].科研管理,2015, 36(3):111-117.(Wang Bo, Liu Shengbo, Ding Kun, et al. Patent Content Analysis Method Based on LDA Topic Model[J]. Science Research Management, 2015, 36(3):111-117.)
[8]吴菲菲,张亚茹,黄鲁成,等.基于AToT模型的技术主题多维动态演化分析——以石墨烯技术为例[J].图书情报工作,2017,61(5):95-102.(Wu Feifei, Zhang Yarn, Huang Lucheng, et al. Multi-dimension Dynamic Evolution Analysis of Technology Topics Based on AToT by Taking Grapheme Technology as an Example[J]. Library and Information Service, 2017, 61(5):95-102.)
[9]廖列法,勒孚刚.基于LDA模型和分类号的专利技术演化研究[J].现代情报,2017,37(5):13-18.(Liao Liefa,Le Fugang. Research on Patent Technology Evolution Based on LDA Model and Classification Number[J]. Modern Information, 2017, 37(5):13-18.)
[10]陈亮,张静,张海超,等.层次主题模型在技术演化分析上的应用研究[J].图书情报工作,2017, 61(5):103-108.(Chen Liang, Zhang Jing, Zhang Haichao, et al. Application of Hierarchical Topic Model on Technological Evolution Analysis[J]. Library and Information Service, 2017, 61(5):103-108.)
[11] Frakes W B, Baeza-Yates R. Information Retrieval:Data Structures and Algorithms[M]. Prentice-Hall, 1992.
[12] Silva C, Ribeiro B. The Importance of Stop Word Removal on Recall Values in Text Categorization[C]//Proceedings of International Joint Conference on Neural Networks,Portland. Washington DC:IEEE Computer Society, 2003:1661-1666.
[13]官琴,邓三鸿,王昊.中文文本聚类常用停用词表对比研究[J].数据分析与知识发现,2017, 1(3):72-80.(Guan Qin,Deng Sanhong,Wang Hao. Chinese Stopwords for Text Clustering:A Comparative Study[J]. Data Analysis and Knowledge Discovery, 2017, 1(3):72-80.)
[14] Crow D, Desanto J. A Hybrid Approach to Concept Extraction and Recognition-based Matching in the Domain of HumanResources[C]//Proceedings of IEEE International Conference on TOOLS with Artificial Intelligence, Boca Raton, USA.Washington DC:IEEE Computer Society, 2004:535-541.
[15] Seki K,Mostafa J. An Application of Text Categorization Methods to Gene Ontology Annotation[C]//Proceedings of the 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval,Salvador, Brazil. New York:ACM Press, 2005:138-145.
[16] Tong S,Lerner U, Singhal A, et al. Locating Meaningful Stopwords or Stop-phrases in Keyword-based Retrieval Systems:US:9817920[P/OL].[2012-07-03].[2017-11-14].https://patents.google.com/patent/US7409383B1/en.
[17] White B J. Impact of Domain-specific Stop-word Lists on ECommerce Website Search Performance[J]. Journal of Strategic E-Commerce, 2007, 5(2):83-102.
[18] Lo T W, He B, Ounis I. Automatically Building a Stopword List for an Information Retrieval System[J]. Journal of Digital Information Management, 2005, 3(1):3-8.
[19] Hao L, Hao L. Automatic Identification of Stop Words in Chinese Text Classification[C]//Proceedings of International Conference on Computer Science and Software Engineering.Washington DC:IEEE Computer Society, 2008:718-722.
[20] Sinka M P, Corne D W. Evolving Better Stoplists for Document Clustering and Web Intelligence[C]//Proceedings of the 3rd International Conference on Hybrid Intelligent Systems, Melbourne, Australia. Amsterdam:IOS Press, 2008:1015-1023.
[21] Jungiewicz M,Lopuszynski M. Unsupervised Keyword Extraction from Polish Legal Texts[C]//Proceedings of the International Conference on Natural Language Processing,Warsaw,Poland. New York:Springer Publishing Company,2014:65-70.
[22] Makrehchi M,Kamel M S. Extracting Domain-specific Stopwords for Text Classifiers[J]. Intelligent Data Analysis,2017,21(1):39-62.
[23]顾益军,樊孝忠,王建华,等.中文停用词表的自动选取[J].北京理工大学学报,2005, 25(4):337-340.(Gu Yijun,Fan Xiaozhong, Wang Jianhua, et al. Automatic Selection of Chinese Stoplist[J]. Transactions of Beijing Institute of Technology, 2005, 25(4):337-340.)
[24]巩政,关高娃.蒙古文停用词和英文停用词比较研究[J].中文信息学报,2011,25(4):35-38.(Gong Zheng,Guan Gaowa. A Comparative Study on Between Mongolian Stop Words and English Stop Words[J]. Journal of ChineseInformation Processing, 2011, 25(4):35-38.)
[25]珠杰,李天瑞.藏文停用词选取与自动处理方法研究[J].中文信息学报,2015, 29(2):125-132.(Zhu Jie,Li Tianrui.Research on Tibetan Stop Words Selection and Automatic Processing Method[J]. Journal of Chinese Information Processing, 2015, 29(2):125-132.)
[26] Blei D M, Ng A Y, Jordan M I. Latent Dirichlet Allocation[J].Journal of Machine Learning Research, 2003, 3(1):993-1022.