Linguistic data classifying method and device, as well as terminal

Abstract

The invention relates to a linguistic data classifying method, a linguistic data classifying device and a terminal. The linguistic data classifying method comprises the following steps: performing word classification on training linguistic data and to-be-classified linguistic data; extracting feature words of each category from the result after the training linguistic data is subjected to word classification; according to the feature words of each category and a training linguistic data training classifier, determining a probability threshold value in the classifier; classifying the to-be-classified linguistic data by utilizing the classifier after training is finished to obtain the classification result. According to the technical scheme of the invention, the accuracy of linguistic material classification is improved.

Description

technical field [0001] The present invention relates to the field of intelligent interaction technology, in particular to a corpus classification method, device and terminal. Background technique [0002] Text classification refers to determining a category for each document in a document collection according to a predefined category. Text classification is an important part of text mining. As a basic research, text classification has applications in many fields, such as information retrieval, automatic classification of Web documents, digital library, automatic summarization, classified newsgroups, text filtering, semantic analysis of words, organization and management of documents, etc. fields. [0003] In the prior art, statistical methods and machine learning methods are usually used for automatic text classification. In text classification, the quality of training sample data and the trained classifier model directly determine the accuracy of classification results. ...

AI Technical Summary

Problems solved by technology

[0004] However, the quality of training samples in the prior art is usually not high, resulting in inaccurate classification results; and when classifying, the classifier model divides the text into the category corresponding to the maximum probability according to the probability that the text belongs to each predefined category. When the probability is still small, it will lead to inaccurate classification results

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