Enterprise text multi-label labeling method and system based on attention mechanism

Abstract

The invention discloses an attention mechanism-based enterprise text multi-label labeling method and system. The method comprises the following steps: acquiring basic attribute information of a data object demander; the obtained information is preprocessed; labeling the preprocessed data by adopting a trained enterprise text multi-label labeling model to obtain a plurality of labeled labels; according to the enterprise text multi-label labeling model, text syntactic and semantic feature extraction is carried out by adopting an attention mechanism layer. According to the enterprise text label labeling method and system, enterprise texts and labels can be automatically labeled, classified and stored, a user can conveniently and accurately inquire enterprise operation content, related personnel can conveniently master the industry distribution dynamic state of local enterprises in real time, the whole process does not need manual intervention, and the system automatically completes the whole process.

Description

technical field [0001] The invention relates to the technical field of data processing, in particular to a method and system for enterprise text multi-label labeling based on an attention mechanism. Background technique [0002] The statements in this section merely provide background related to the present disclosure and do not necessarily constitute prior art. [0003] At present, most of the industry labeling and classification schemes of enterprises rely on manual data labeling and classification. Therefore, personal subjective factors will be incorporated into the classification. With the increasing number of enterprises, there will be problems such as high labeling cost and incomplete coverage. . At this stage, some traditional machine learning methods are used, but the traditional classification methods are not satisfactory, and the generalization ability is not strong. There are also a small number of people who use deep learning classification methods, but only ...

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Problems solved by technology

[0003] At present, most of the industry labeling and classification schemes of enterprises rely on manual data labeling and classification, so personal subjective factors will be mixed into the classification. With the increasing number of enterprises, there will still be problems such as high labeling costs and incomplete coverage.

At this stage, some traditional machine learning methods are used, but the effect of traditional classification methods is not ideal, and the generalization ability is not strong

There are also a small number of people who use deep learning classification methods, but only for the business scope of the company, resulting in the inability to fully reflect the true state of the company and the inaccurate classification of the company's industry

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