Affiliation:
1. School of Computer Science and Engineering, Xi’an Technological University , Xi’an , , China
Abstract
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.
Subject
General Earth and Planetary Sciences,General Environmental Science
Reference9 articles.
1. Wang Ning, GE Ruifang, Yuan Chunfa, Huang Jinhui, LI Wenjie. Recognition of company names in Chinese Financial News., 2002(02):1–6.
2. Wang S, Xu R, Liu B, et al. Financial named entity recognition based on conditional random fields and information entropy[C]//2014 International Conference on Machine Learning and Cybernetics. Lanzhou: IEEE, 2014: 838-843.
3. Qian-yu li. Financial entity oriented knowledge map construction research [D]. Yunnan university of finance and economics, 2020, DOI: 10.27455 /, dc nki. Gycmc. 2020.000492.
4. Wang Guoming. Based on the deep study of the financial sector knowledge map construction research [D]. The northeast normal university, 2021. The DOI: 10.27011 /, dc nki. Gdbsu. 2021.000237.
5. He Yunqi, Liu Suwen, Qian Longhua, et al. Disease Name Recognition Based on Syntactic and Semantic features [J]. Science China Information Science, 2018, 48 (11): 1546-1557.