<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>In order to obtain the knowledge information of financial texts more efficiently and make the extracted information such as entity relation attribute more accurate, this paper studies the grammatical features of financial news texts and the semantic features of How Net, and puts forward the scheme of financial information extraction based on How Net. First, the phrase matching is carried out in the dictionary. Then the neural network is used for weighting, BiLSTM is used for character vector feature enhancement training, and then conditional random field (CRF) is used to complete named entity recognition, and then the relationship extraction of entity pairs from the dependency syntax is carried out to complete the research on the construction method of knowledge extraction of text in the financial field. The experimental results show that this model is superior to the other three models in entity recognition, and the overall performance is improved by about 1.2%. In relation extraction, the accuracy and recall rate of the model algorithm adopted in this paper are improved by 5% and 1.5% respectively, which shows that the improvement of the algorithm is effective.</p>
</abstract>

In order to obtain the knowledge information of financial texts more efficiently and make the extracted information such as entity relation attribute more accurate, this paper studies the grammatical features of financial news texts and the semantic features of How Net, and puts forward the scheme of financial information extraction based on How Net. First, the phrase matching is carried out in the dictionary. Then the neural network is used for weighting, BiLSTM is used for character vector feature enhancement training, and then conditional random field (CRF) is used to complete named entity recognition, and then the relationship extraction of entity pairs from the dependency syntax is carried out to complete the research on the construction method of knowledge extraction of text in the financial field. The experimental results show that this model is superior to the other three models in entity recognition, and the overall performance is improved by about 1.2%. In relation extraction, the accuracy and recall rate of the model algorithm adopted in this paper are improved by 5% and 1.5% respectively, which shows that the improvement of the algorithm is effective.

Research on Extraction Method of Financial Knowledge Based on How Net

International Journal of Advanced Network, Monitoring and Controls

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

{"article-title":"Research on Extraction Method of Financial Knowledge Based on How Net"}

In order to obtain the knowledge information of financial texts more efficiently and make the extracted information such as entity relation attribute more...

Model	Precision	Recall	F1
LSTM	35.8	42.4	38.8
Bi-LSTM	57.5	55.4	56.4
Bi-LSTM+self-ATT	65.2	56.8	60.7
This algorithm	70.2	58.3	63.7

Participles	The part of speech	How Net Semantics	BIO
China	ns	Local, national	B-ORG
Internet	n	The Internet	I-ORG
Finance	n	Business, money	I-ORG
Association	n	group	I-ORG
With	p	Function words	O
Ministry of Civil Affairs	nt	institutions	B-ORG
approval	v	Give one’s consent	O
Shanghai	ns	place	B-ADDR
registered	v	record	O
establishe	v	To establish	O

Research on Extraction Method of Financial Knowledge Based on How Net

Published Online: May 31, 2023

Page range: 50 - 57

DOI: https://doi.org/10.2478/ijanmc-2023-0045

Keywords
Named Entity Identification in the Financial Sector, Relation Extraction, How Net

© 2023 Chaoyang Geng et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Figure 1.

Figure 2.

Figure 3.

Figure 4.

Comparison of experimental results

“China… “Is valid

Research on Extraction Method of Financial Knowledge Based on How Net

Published Online: May 31, 2023

Page range: 50 - 57

DOI: https://doi.org/10.2478/ijanmc-2023-0045

KeywordsNamed Entity Identification in the Financial Sector, Relation Extraction, How Net

© 2023 Chaoyang Geng et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Figure 1.

Figure 2.

Figure 3.

Figure 4.

Comparison of experimental results

“China… “Is valid

Keywords
Named Entity Identification in the Financial Sector, Relation Extraction, How Net