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Data Analysis and Knowledge Discovery  2018, Vol. 2 Issue (11): 95-103    DOI: 10.11925/infotech.2096-3467.2018.0240
Current Issue | Archive | Adv Search |
Choosing Stopwords for Patent Topic Analysis Based on Auxiliary Set
Yu Yan1,2(), Zhao Naixuan1
1Information Service Department, Nanjing Tech University, Nanjing 210009, China
2Department of Computer Engineering, Southeast University Chengxian College, Nanjing 211816, China
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Abstract  

[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.

Key wordsPatent Topic Analysis      Domain Specific Stopwords      Auxiliary Set     
Received: 05 March 2018      Published: 11 December 2018
ZTFLH:  G250  

Cite this article:

Yu Yan,Zhao Naixuan. Choosing Stopwords for Patent Topic Analysis Based on Auxiliary Set. Data Analysis and Knowledge Discovery, 2018, 2(11): 95-103.

URL:

http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2018.0240     OR     http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2018/V2/I11/95

数据集类型 类别 文本量
辅助集1 A 人类生活必需(农、轻、医) 2000
B 作业; 运输 2000
C 化学; 冶金 2000
D 纺织; 造纸 2000
E 固定建筑物(建筑、采矿) 2000
F 机械工程; 照明; 加热; 武器; 爆破 2000
辅助集2 G01 测量; 测试 2000
G02 光学 2000
G03 摄影术; 电影术; 利用了光波以外其它波的类似技术; 电刻术; 全息摄影术 2000
G07 核算装置 2000
G08 信号装置 2000
G09 教育; 密码术; 显示; 广告; 印鉴 2000
G11 信息存储 2000
数据集 方法 领域停用词
目标集 TF 语音 模块 所述 识别 控制 一种 本发明 方法 信息 包括 装置 进行 用户 系统 智能 信号 中 连接 数据 用于
DF 语音 一种 本发明 包括 识别 进行 方法 所述 公开 控制 装置 中 系统 模块 用户 信息
提供 实现 接受 智能
TF-IDF 模块 所述 信息 控制 智能 信号 单元 装置 用户 终端 设备 数据 系统 机器人 识别 方法 第一 用于 连接 音频
辅助集1 ASDF 一种 本发明 包括 上 公开 所述 中 连接
方法 设置 具有 涉及 装置 进行 设有 结构 内 后 提供 提高
ASEC 公开 涉及 具有 中 所述 装置 进行 提供
连接 上 提高 设置 后 结构 内 简单 设有
技术 效果 领域
辅助集2 ASDF 一种 本发明 包括 所述 方法中 提供 上 公开 装置 进行 用于具有 系统 连接 控制 第一
设置 时 涉及
ASEC 提供 中 装置 上 公开 所述 用于 方法 进行 具有 涉及 时 连接 设置 控制 第一 系统
能够 实现 技术
数据集 方法 目标集 辅助集1 辅助集2
TF DF TF-IDF ASDF ASEC ASDF ASEC
目标集 TF
DF 81
TF-IDF 88 69
辅助集1 ASDF 42 49 34
ASEC 40 49 34 95
辅助集2 ASDF 63 71 54 62 61
ASEC 60 70 52 64 65 93
数据集 停用词选取方法 主题模型
目标集 通用停用词+TF TF_LDA
通用停用词+DF DF_LDA
通用停用词+TF-IDF TFIDF_LDA
辅助集1 通用停用词+ASDF ASDF1_LDA
通用停用词+ASEC ASEC1_LDA
辅助集2 通用停用词+ASDF ASDF2_LDA
通用停用词+ASEC ASEC2_LDA
主题 gen-LDA ASEC1_LDA
0 语音 识别 输入 用于 发明 数据 音频 识别 语音 生成
1 系统 交互 智能 机器人 基于 发明 特征 模型 合成 训练 解码
2 模块 系统 语音 技术 计算机 模块 电路 无线 传感器 通信
3 信号 连接 电路 述 发明 计算机 汉语 方案 输入 程序
4 控制 语音 指令 发明 用于 语音 装置 检测 判断 车载
5 数据 中 方法 文本 音频 包括 信息 移动 服务器 发送 播放
6 语音 方法 特征 模型 进行 语音 信号 输入 输出 声音
7 发明 进行 检测 时 识别 方法 系统 交互 机器人 智能 平台
8 信息 用户 语音 方法 述 装置 安装 电子 开关 显示屏
9 述 装置 上 智能 包括 语音 指令 命令 智能家居 遥控器
[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.
doi: 10.1007/s11192-014-1342-3
[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.
doi: 10.1016/j.cie.2016.06.006
[5] Suominen A, Toivanen H, Seppänen M.Firms’ Knowledge Profiles: Mapping Patent Data with Unsupervised Learning[J]. Technological Forecasting & Social Change, 2016, 115: 131-142.
doi: 10.1016/j.techfore.2016.09.028
[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.
[6] (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.
[7] (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.
doi: 10.13266/j.issn.0252-3116.2017.05.013
[8] (Wu Feifei, Zhang Yaru, 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.)
doi: 10.13266/j.issn.0252-3116.2017.05.013
[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.
doi: 10.3969/j.issn.1008-0821.2017.05.003
[9] (Liao Liefa, Le Fugang.Research on Patent Technology Evolution Based on LDA Model and Classification Number[J]. Modern Information, 2017, 37(5): 13-18.)
doi: 10.3969/j.issn.1008-0821.2017.05.003
[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.
doi: 10.13266/j.issn.0252-3116.2017.05.014
[10] (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.)
doi: 10.13266/j.issn.0252-3116.2017.05.014
[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.
[13] (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 Human Resources[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]. .
[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, Łopuszyński 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.
doi: 10.3233/IDA-150390
[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.
doi: 10.3969/j.issn.1001-0645.2005.04.014
[23] (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.)
doi: 10.3969/j.issn.1001-0645.2005.04.014
[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.
doi: 10.7666/d.y1887441
[24] (Gong Zheng, Guan Gaowa.A Comparative Study on Between Mongolian Stop Words and English Stop Words[J]. Journal of Chinese Information Processing, 2011, 25(4): 35-38.)
doi: 10.7666/d.y1887441
[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.
doi: 10.3969/j.issn.1003-0077.2015.02.015
[25] (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.)
doi: 10.3969/j.issn.1003-0077.2015.02.015
[26] Blei D M, Ng A Y, Jordan M I.Latent Dirichlet Allocation[J]. Journal of Machine Learning Research, 2003, 3(1): 993-1022.
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