Abstract
To extract effective information in massive financial news, this paper proposes a method to calculate the correlation between text and text set by extracting structured events in the stock market news text, and provides more detailed and interpretable information. First, the structured event triplet was extracted from the text set, and the trained word vector was used to represent the event triplet as an event vector. Event vectors were clustered, the cosine distances were calculated for the cluster centers, and the correlation between the text sets was determined by matching. Finally, the event triplets with the highest correlation between the text sets were selected to provide explanation information for the calculation results. Experimental results show that this method effectively measures the correlation between text and text set.
J. Wu and Y. Wang—Contributed equally to this work. This work is supported by: Engineering Research Center of State Financial Security, Ministry of Education, Central University of Finance and Economics, Beijing, 102206, China; Program for Innovation Research in Central University of Finance and Economics.
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Wu, J., Wang, Y. (2021). A Text Correlation Algorithm for Stock Market News Event Extraction. In: Zeng, J., Qin, P., Jing, W., Song, X., Lu, Z. (eds) Data Science. ICPCSEE 2021. Communications in Computer and Information Science, vol 1452. Springer, Singapore. https://doi.org/10.1007/978-981-16-5943-0_5
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DOI: https://doi.org/10.1007/978-981-16-5943-0_5
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