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
[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.
(Zhong Maosheng, Liu Hui, Liu Lei. Method of Semantic Relevance Relation Measurement Between Words[J]. Journal of Chinese Information Processing, 2009, 32(2): 115-122.)
(Qiu Keda, Ma Jianling. A Review of Hypernym Relation Recognition from Text Corpora[J]. Information Science, 2020, 38(7): 162-172.)
[4]
Hearst M A. Automatic Acquisition of Hyponyms from Large Text Corpora [C]//Proceedings of the 14th Conference on Computational Linguistics - Volume 2. 1992: 539-545.
(Li Junfeng, Lv Xueqiang, Li Zhuo. Deriving Concept Semantic Hierarchy of Ontology in Patents[J]. Journal of the China Society for Scientific and Technical Information, 2014, 33(9): 986-993.)
(Zhang Chentong, Zhang Jiaying, Zhang Zhixing, et al. Construction of Large-Scale Disease Terminology Graph with Common Terms[J]. Journal of Computer Research and Development, 2020, 57(11): 2467-2477.)
[8]
Snow R. Learning Syntactic Patterns for Automatic Hypernym Discovery [C]//Proceedings of the 17th International Conference on Neural Information Processing Systems. 2005: 1297-1304.
[9]
Suchanek F M, Kasneci G, Weikum G. YAGO: A Large Ontology from Wikipedia and WordNet[J]. Journal of Web Semantics, 2008, 6(3): 203-217.
doi: 10.1016/j.websem.2008.06.001
(Lu Kaihua, Li Zhenghua, Zhang Min. Data Construction and Benchmark Method Comparison for Chinese Hypernym-Hyponym Relation Classification[J]. Journal of Xiamen University (Natural Science Edition), 2020, 59(6): 1004-1010.)
(Ding Shengchun, Hou Linlin, Wang Ying. Product Knowledge Map Construction Based on the E-Commerce Data[J]. Data Analysis and Knowledge Discovery, 2019, 3(3): 45-56.)
(Huang Yi, Wang Qinglin, Liu Yu. An Acquisition Method of Domain-Specific Terminological Hyponymy Based on CRF[J]. Journal of Central South University (Science and Technology), 2013, 44(S2): 355-359.)
(Ma Xiaojun, Guo Jianyi, Xian Yantuan, et al. Entity Hyponymy Acquisition and Organization Combining Word Embedding and Bootstrapping in Special Domain[J]. Computer Science, 2018, 45(1): 67-72.)
(Sun Jiawei, Li Zhenghua, Chen Wenliang, et al. Hypernym Relation Classification Based on Word Pattern[J]. Acta Scientiarum Naturalium Universitatis Pekinensis, 2019, 55(1): 1-7.)
(Wu Ting, Li Mingyang, Kong Fang. Construction of Textual Entity Hypernymy Corpus Based on Synonymy Reasoning[J]. Journal of Chinese Information Processing, 2020, 34(4): 38-46.)
(Wang Chengyu, He Xiaofeng, Gong Xueqing, et al. Word Embedding Projection Models for Hypernymy Relation Prediction[J]. Chinese Journal of Computers, 2020, 43(5): 868-883.)
[17]
Kotlerman L, Dagan I, Szpektor I, et al. Directional Distributional Similarity for Lexical Expansion [C]//Proceedings of ACL-IJCNLP 2009 Conference (Short Papers). 2009: 69-72.
(Wang Sili, Zhu Zhongming, Yang Heng, et al. Automatically Identifying Hypernym-Hyponym Relations of Domain Concepts with Patterns and Projection Learning[J]. Data Analysis and Knowledge Discovery, 2020, 4(11): 15-25.)
(Wu Zhixiang, Wang Hao, Wang Xueying, et al. Study on Chinese Patent Terms Hierarchy Parse Based on Singular Value Decomposition[J]. Journal of the China Society for Scientific and Technical Information, 2017, 36(5): 473-483.)
[20]
Kozareva Z, Hovy E. A Semi-Supervised Method to Learn and Construct Taxonomies Using the Web [C]//Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing. 2010: 1110-1118.
[21]
Fu R J, Qin B, Liu T. Exploiting Multiple Sources for Open-Domain Hypernym Discovery [C]//Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing. 2013: 1224-1234.
(Liu Qi, Xiao Yanghua, Wang Wei. A Recognition Approach of Typical Generic Relationship for Massive Chinese Text[J]. Computer Engineering [J]. Computer Engineering, 2015, 41(2): 26-30.)
(Gan Lixin, Wan Changxuan, Liu Dexi, et al. Chinese Named Entity Relation Extraction Based on Syntactic and Semantic Features[J]. Computer Research and Development, 2016, 53(2): 284-302.)
[24]
Bansal M, Burkett D, de Melo G, et al. Structured Learning for Taxonomy Induction with Belief Propagation [C]// Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2014: 1041-1051.
(Duan Liguo, Xu Qing, Li Aiping, et al. Research on Effect of Entities Semantic Information on Chinese Entity Relation Extraction[J]. Computer Application Research, 2017, 34(1): 141-146.)
[26]
Mikolov T, Yih S W T, Zweig G. Linguistic Regularities in Continuous Space Word Representations [C]//Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2013.
[27]
付瑞吉. 开放域命名实体识别及其层次化类别获取[D]. 哈尔滨: 哈尔滨工业大学, 2014.
[27]
(Fu Ruiji. Open-Domain Named Entity Recognition and Hierarchical Category Acquisition[D]. Harbin: Harbin Institute of Technology, 2014.)
[28]
Lai S W, Liu K, He S Z, et al. How to Generate a Good Word Embedding[J]. IEEE Intelligent Systems, 2016: 31(6): 5-14.