Information classification extraction method and device, computer equipment and storage medium

Abstract

The invention relates to an information classification extraction method and device based on a classifier, computer equipment and a storage medium. The information classification extraction method comprises the steps of obtaining to-be-classified information; performing word segmentation processing on the to-be-classified information to obtain a plurality of text blocks; inputting the plurality oftext blocks into a trained classifier, classifying the plurality of text blocks, and determining a target event category and a plurality of event elements of the to-be-classified information; extracting corresponding event element parameters according to the target event category and the event elements, and outputting the target event category, the plurality of event elements and the corresponding event element parameters; and generating event atlas data corresponding to the target event category according to the plurality of event elements and the corresponding event element parameters. By adopting the information classification extraction method, the event information can be accurately and effectively classified, and the key event information can be extracted.

Description

technical field [0001] The present application relates to the field of computer technology, in particular to an information classification and extraction method, device, computer equipment and storage medium. Background technique [0002] With the rapid development of Internet technology, some news events may occur all over the world at any time, and some news event information has important research value. Artificially obtaining some key information in event information brings too much workload, so there are some ways to extract key event information from natural language texts. [0003] Most of the existing methods use various features to introduce memory vectors and matrices, introduce more transition arcs, and use sentence-level sequential RNN modeling methods and CRF algorithms to retain more context information. Another approach is to use feature-seeking at the document level. However, the sentence-level sequential modeling method is inefficient in capturing very lon...

AI Technical Summary

Problems solved by technology

However, the sentence-level sequence modeling method is inefficient in capturing very long-distance dependencies, and the feature-based method requires heavy manual intervention, which greatly affects the performance of the model, and cannot accurately and effectively associate events. Recognition and classification accuracy is not high

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