Document classification method and document classification device

Abstract

The invention discloses a document classification method and a document classification device. The document classification method includes the steps: extracting feature text of a target document and utilizing the feature text to form search conditions; searching by utilizing the search conditions to acquire a relevant search result; calculating text similarity of the target document and the search result; acquiring a classification result of the target document according to the acquired text similarity by calculating and classification information of the research result. Based on the similarity among the texts and by utilizing existed document classification information to perform classification on the new document, the classification result high in confidence coefficient can be acquired through statistical computation upon text classification similar to current text content due to the fact that documents similar in the text content are high in probability of belonging to the same classification.

Description

technical field [0001] The invention relates to the field of computer application technology, in particular to a document classification method and device. Background technique [0002] With the development of Internet technology, the amount of information on the Internet has exploded. In order to apply these information better, it is necessary to manage these information data effectively. Among them, document classification (document classification) is currently a widely used management technology. Document classification refers to determining a category for each document in the document collection according to the content or certain attributes of the document. In this way, users can not only browse documents in a specific category conveniently, but also make finding documents easier by limiting the search scope. [0003] For massive document resources, it is obviously unrealistic to use a completely manual classification method for classification. At present, on some U...

AI Technical Summary

Problems solved by technology

The disadvantage of this method is that it requires users to fully understand the classification system to provide accurate classification information, which increases the user's operating costs, and in practical applications, some users do not seriously provide classification information, and the system cannot recognize this. In this case, it will affect the accuracy of the classification system

However, in practical applications, due to the consideration of labor costs, the scale of selected labeled samples is often limited, resulting in inaccurate classification feature extraction, which will affect the classification effect to a certain extent.

On the other hand, in the face of the rapid update of information, the labeled corpus should also be updated accordingly to ensure the classification effect, but to continuously obtain the labeled corpus also requires a lot of labor costs

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