Enterprise industry classification method based on fully-automatic learning

Abstract

The invention relates to the field of natural language processing, and particularly relates to an enterprise industry classification method based on fully-automatic learning. The method of the invention uses a recurrent neural network to carry out fully-automatic feature learning on a business scope of a to-be-classified enterprise and automatically divides the to-be-classified enterprise to a corresponding industry scope. The method breaks domain barriers of the natural language and the neural network technology, and automatic enterprise industry classification based on natural language analysis is realized. The method does not need to manually select features, the defect that manual feature selection in the traditional method deviates from a specific sample is solved, the developer pays more attention to the task itself and data reserve, the working efficiency is enhanced, and as the recurrent neural network is used, longer sequence features can be captured by the model, the industry classification accuracy can be greatly enhanced, and more accurate basic data are provided for business and finance analysis.

Description

technical field [0001] The invention relates to the field of natural language processing, in particular to a method for classifying enterprises and industries based on fully automatic learning. Background technique [0002] With the progress of society and the prosperity and development of the market, China's economy has been on a high-speed development track. As the most important activity subject in the social economy, enterprises play an important role in the economy. Help relevant decision makers understand the business status of the company and discover potential business risks. The analysis of the main body of the enterprise is inseparable from the definition of industry ownership. Enterprises in different industries have certain common industry characteristics and industry attributes; economists and financial experts usually hope to understand the respective characteristics of each industry when calculating national economic indicators. Condition. Nowadays, there ar...

AI Technical Summary

Problems solved by technology

Traditional feature extraction methods are difficult to capture such sequence-dependent features. This is because when N is relatively large, the scale of the N-gram language model is too large (that is, the Nth power of the dictionary size), which directly leads to the extraction of features. Dimensions cannot be used for subsequent classification tasks

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