Plain text oriented enterprise entity classification method

Abstract

The invention discloses a plain text oriented enterprise entity classification method. The plain text oriented enterprise entity classification method comprises the steps of S1, carrying out type labeling for the enterprise entities in collected plain text data and regarding the enterprise entities being subjected to type labeling as a training set of an enterprise entity identification module; carrying out type labeling for the enterprise entities in the collected plain text data according to business nature and regarding the enterprise entities being subjected to the type labeling as a training sample set of an enterprise entity classification module; and S2, carrying out enterprise entity identification model training through a condition random field model to obtain an enterprise entity identification model; S3, carrying out semantic vectorization construction for the text data of an original training set; S4, training by regarding the data of the training set after being subjected to type labeling and semantic vectorization as training parameters to obtain an enterprise entity classification model; and S5, classifying the enterprise entity in a to-be-predicted text by utilizing the enterprise entity classification model. According to the plain text oriented enterprise entity classification method, as the obtained semantic vector serves as the feature of the entity, dependence on artificial features and external data is reduced, and the universality and robustness are guaranteed.

Description

technical field [0001] The invention belongs to the technical field of named entity recognition and fine-grained entity classification, and in particular relates to a plain text-oriented enterprise entity classification method. Background technique [0002] In recent years, with the upsurge of "Internet finance", more and more corporate decision makers urgently need to use more advanced information processing methods to extract and analyze massive Internet data in order to make better decisions. Among these massive data, plain text data such as court documents and news and public opinion have become the primary source for enterprises to obtain high-value information. [0003] Named entity recognition technology is the basis for enterprises to carry out entity semantic analysis and entity relationship extraction. The current mainstream named entity recognition technology only divides entities into person names, place names, organization names, etc., which makes the types of ...

AI Technical Summary

Problems solved by technology

The current mainstream named entity recognition technology only divides entities into person names, place names, organization names, etc., which makes the types of entities lack semantics.

At the same time, when entity classification relies too much on artificial features and external data, its versatility and robustness cannot be guaranteed.

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