Extracting Hypernym-Hyponym Relationship for Financial Market Applications

doi:10.11925/infotech.2096-3467.2020.1261

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 Xiaoling^1,²

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 words： Hypernym-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

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	Xiaoling Hao

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:

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

序号	实例
1	$v 虾 - v 对虾 ≈ v 鱼 - v 金鱼$
2	$v 工人 - v 木匠 ≈ v 演员 - v 小丑$
3	$v 工人 - v 木匠 ≠ v 鱼 - v 金鱼$

Results of Vector Shift for Hypernym-Hyponym Word Pair

Hypernym-Hyponym Extraction Method Based on Matrix Mapping and Word Vector

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

Comparison of the Method in This Paper with Previous Research Methods

Main Business Composition of Shenhua China by Industry and Product

List of Extracted Industry and Product Words

Extracted Entity Words in Internet Domain

Extracted Hypernym-Hyponym Word Pairs

Hierarchical Structure of Internet Products and Services

Correlation Coefficients of Daily Return and Daily Excess Return of Three Groups of Stocks

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