Text classification method combining title and text attention mechanism

Abstract

The invention discloses a text classification method combining a title and a text attention mechanism. The method comprises the following steps: firstly, carrying out word segmentation preprocessing on a title and a main body of each document to obtain a title word set and a main body word set; training a word vector by adopting a word2vec CBOW model, the expression of each word combined with context semantics is learned by using a bidirectional recurrent neural network, and the potential semantic vector of one word is obtained through serial word vectors and the expression of left and right contexts of the word vectors; respectively carrying out maximum pooling processing on the potential semantic vectors of each word in the title word set and the text word set to obtain a title vector and a text vector; obtaining an attention vector by using a title and text attention mechanism; and after the vector representation of the whole document is calculated, outputting the category of the probability prediction text through a softmax function. The method solves the problem that the classification result is low in accuracy because the importance of the title content is ignored and the title is taken as one part of the text or the title information is ignored in the existing text classification with the title.

Description

Technical field [0001] The invention belongs to the field of natural language processing, and in particular relates to a text classification method that combines a headline and body attention mechanism. Background technique: [0002] With the rapid development of information platforms in all walks of life in our country, online text data has become immense. Massive text data contains important value, but how to efficiently organize and use these data has become a major problem. Text classification technology in natural language processing is an effective solution. [0003] The text classification technology is a process of constructing a classifier model from the first text data, and classifying the new text according to the established classifier model. Traditional text classification methods focus on the two major issues of feature engineering and selection of classifiers. There are problems such as high dimensionality and high sparseness of text representation, weak feature exp...

AI Technical Summary

Problems solved by technology

[0008] The purpose of the present invention is to provide a text classification method that combines the title and text attention mechanism, which solves the problem that the existing title-containing text classification method ignores the importance of the title content, treats the title as a part of the text or directly ignores the title information, leading to the problem of low accuracy of classification results

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