Content-based problem automatic classifying method and system

Abstract

The invention discloses a content-based question automatic classification method and a content-based question automatic classification system. The system comprises a question key word acquisition module, a characteristic space construction module and a semantic mapping module, wherein, the question key word acquisition module is used for acquiring a question key word of a novel question according to a key word label and/or a fillable content label in a template, setting a weight for the question key word and obtaining a question vector of the novel question; the characteristic space construction module is used for acquiring a characteristic vector of each class according to questions of all the prior classes and weights and constructing a characteristic space; the semantic mapping module is connected with the characteristic space construction module and the question key word acquisition module and used for mapping the question vector of the novel problem to the characteristic space, calculating the similarity of the novel problem and each class according to the question vector of the novel question after mapping and the characteristic vector of each class, and returning classes which are most related to the novel problem according to the similarity. The content-based question automatic classification method and the content-based question automatic classification system realize automatic classification of the novel problem which is put forward by a user and return most probable results to the user for selection.

Description

Technical field [0001] The present invention relates to classification technology, in particular to a method and system for automatically classifying problems based on content. Background technique [0002] The traditional text similarity calculation method uses its word frequency vector (or term-frequency vector) to express the text, and then calculates the distance between the word frequency vectors to obtain the text similarity. However, most of the existing methods for calculating text similarity are only suitable for long texts. The traditional method for calculating the similarity of long texts is effective because similar long texts usually contain a certain number of the same vocabulary. But for short texts such as questions, similar questions do not necessarily have the same words. The flexibility of natural language allows people to express the same meaning through different words. Therefore, the existing similarity calculation methods are not effective. . In addition, ...

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Problems solved by technology

[0007] The technical problem to be solved by the present invention is to provide a content-based automatic question classification method and its system, which are used to

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