Event extraction method and device in public opinion monitoring in financial field and computer equipment

Abstract

The invention relates to an event extraction method and device in public opinion monitoring in the financial field and computer equipment. The method comprises the steps of obtaining sample data froma financial news text, and preprocessing the sample data to obtain a sample set; obtaining a plurality of preset different event extraction models, and training the event extraction models in a K-foldcross validation mode according to the sample set to obtain K event extraction sub-models of each event extraction model; preprocessing the to-be-extracted text, inputting the preprocessed to-be-extracted text into K event extraction sub-models of each event extraction model, outputting a prediction text ID, a prediction event and a prediction entity corresponding to the to-be-extracted text, andconstructing a triple according to the prediction text ID, the prediction event and the prediction entity; and voting triples output by the K event extraction sub-models of each event extraction model in a voting mode to determine a real triad. By adopting the method, accumulated errors can be reduced.

Description

technical field [0001] The present application relates to the field of computer technology, in particular to an event extraction method, device and computer equipment in public opinion monitoring in the financial field. Background technique [0002] "Event extraction" is one of the important tasks in the field of public opinion monitoring and the financial field. "Event" is an important decision-making reference for investment analysis and asset management in the financial field; events are also an important part of knowledge graphs. Event extraction is the graph reasoning, Necessary process for event analysis. Due to the complexity of the text and the complexity of the task, there are still many challenges in the task of event extraction in the financial field. There are mainly two models for the event extraction task, namely the Pipeline model and the Joint model. The former first extracts the event, then identifies the event subject, or finally determines the event. Th...

AI Technical Summary

Problems solved by technology

However, it ignores the internal connection and dependency between the two tasks, and there may be error accumulation problems

The joint model can make full use of the potential information between the two tasks, and can alleviate the shortcomings of pipeline model error accumulation, but there is still an error accumulation problem in its inference.

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