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Data Analysis and Knowledge Discovery  2021, Vol. 5 Issue (10): 60-70    DOI: 10.11925/infotech.2096-3467.2020.1261
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Extracting Hypernym-Hyponym Relationship for Financial Market Applications
Dai Zhihong1(),Hao Xiaoling1,2
1School of Information Management & Engineering, Shanghai University of Finance and Economics, Shanghai 200433, China
2Shanghai Key Laboratory of Financial Information Technology, Shanghai University of Finance and Economics, Shanghai 200433, China
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Abstract  

[Objective] This paper proposes a new method to extract superior-inferior relationship from knowledge graph, and then explores its effectiveness with practical application. [Methods] First, we constructed the mapping matrix for hypernym-hyponym words and their context semantics. Then, we combined word vector similarity with the matrix to extract the relation. [Results] We examined our method with datasets of listed companies and found its F1 value was more than 3% higher than those of the existing methods. The new model could help us study the association between company similarity and stock performance. [Limitations] More research is needed to improve relationship extraction with the help of clustering technique and pattern matching method. [Conclusions] The proposed method can effectively identify the relationship between entities, and study the related listed companies and stocks. It also helps us construct better knowledge graph in the financial field.

Key wordsHypernym-Hyponym Relationship      Relationship Extraction      Word Vector      Stock Linkage     
Received: 16 December 2020      Published: 27 August 2021
ZTFLH:  TP391  
Fund:National Social Science Foundation of China(20BGL287);National Natural Science Fund of China(71401096)
Corresponding Authors: Dai Zhihong, ORCID:0000-0002-3890-115X     E-mail: daizhihong@189.cn

Cite this article:

Dai Zhihong, Hao Xiaoling. Extracting Hypernym-Hyponym Relationship for Financial Market Applications. Data Analysis and Knowledge Discovery, 2021, 5(10): 60-70.

URL:

http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2020.1261     OR     http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2021/V5/I10/60

序号 实例
1 v - v 对虾 v - v 金鱼
2 v 工人 - v 木匠 v 演员 - v 小丑
3 v 工人 - v 木匠 v - v 金鱼
Results of Vector Shift for Hypernym-Hyponym Word Pair
Hypernym-Hyponym Extraction Method Based on Matrix Mapping and Word Vector
词语 余弦相似度 词语 余弦相似度
上海 1.000 0 上海财经大学 1.000 0
杭州 0.677 0 财经大学 0.544 3
上海市 0.676 6 复旦大学 0.540 7
苏州 0.645 4 上海交通大学 0.517 7
宁波 0.624 0 华东师范大学 0.511 0
天津 0.616 6 清华大学 0.508 3
Top Five Closest Words in the Word Vector Space
Influence of Lower Bound of Similarity and Number of Clusters on the F1 Value
参数 取值
δ 1 0.5
δ 2 6
细分区间及相似度下限 1.5 < d 2,0.15/0.2
2 < d 4,0.25/0.3
4 < d 6,0.35/0.4
聚类类别数K 60
Related Parameters
指标 δ + Simi 映射偏离 δ 词向量相似度 Simi
准确率 0.835 4 0.643 6 0.716 7
召回率 0.834 7 0.815 2 0.805 4
F1值 0.835 0 0.719 3 0.758 4
Test Set Judgment Results
方法 准确率 召回率 F1值
Hearst[4] 0.974 7 0.214 1 0.351 1
Snow[8] 0.608 8 0.256 7 0.361 1
Suchanek等[9] 0.924 1 0.606 1 0.732 0
Fu等[21] 0.797 8 0.808 1 0.802 9
本文 0.835 4 0.834 7 0.835 0
Comparison of the Method in This Paper with Previous Research Methods
分类依据 主营构成 主营收入/亿元
按行业分类 发电 960.31
铁路 364.32
煤化工
港口
185.06
29.97
航运 14.48
未分配项目
分部抵消
11.89
-388.81
按产品分类 煤炭收入
发电收入
其他收入
运输收入
煤化工收入
755.15
358.86
34.50
29.50
27.17
Main Business Composition of Shenhua China by Industry and Product
公司 行业 产品
科大讯飞 信息技术、教育 信息工程、教育软件、人机交互
四维图新 导航、芯片、应用 导航、芯片、应用
恒源煤电 工业 煤炭、电力
三友化工 化工、电、采矿业 短纤维、纯碱、聚氯乙烯
List of Extracted Industry and Product Words
互联网产品与服务实体词
网络服务 流媒体
电子商务 网络游戏
网络安全 手游
即时通信 电子邮件
网络营销 防病毒
物流 防火墙
供应链 电竞
快递 聊天工具
浏览器 电脑游戏
搜索引擎 电子竞技
Extracted Entity Words in Internet Domain
下位词 上位词 下位词 上位词
电子商务 网络服务 手游 网络游戏
网络安全 网络服务 防病毒 网络安全
即时通信 网络服务 防火墙 网络安全
物流 电子商务 网络安全 网络服务
供应链 电子商务 电子邮件 即时通信
流媒体 网络服务 电脑游戏 网络游戏
搜索引擎 网络服务 电子竞技 网络游戏
浏览器 网络服务 聊天工具 即时通信
网络游戏 网络服务
Extracted Hypernym-Hyponym Word Pairs
Hierarchical Structure of Internet Products and Services
电商板块股票 物流板块股票 日收益率
相关系数
日超额收益率相关系数
苏宁云商 顺丰控股 0.5915 0.4182
焦点科技 飞马国际 0.2612 0.1928
众应互联 韵达股份 0.2217 0.1632
Correlation Coefficients of Daily Return and Daily Excess Return of Three Groups of Stocks
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