Word-sentence-level short text classification method based on deep learning

Abstract

The invention discloses a word-sentence-level short text classification method based on deep learning, and belongs to the technical field of natural language processing. According to the method, wordfeatures and sentence features are combined based on a word vector technology to express complex text features. By performing convolution pooling on the word vectors in the single sentence through a plurality of convolution kernels of the convolutional neural network, all the feature maps are connected to obtain sentence vectors, and the sentence vectors are inputted into the long-term and short-term memory network according to a time sequence to perform context association so as to better express text contents. To-be-classified short text data is subjected to sentence segmentation, word segmentation, stop word removal, word vector conversion and the like and then input into a word-sentence level convolutional recurrent neural network for training, and finally, a short text classificationmodel can be obtained and a short text classification task is completed. The method has good performance in the aspects of tested Chinese spam e-mail classification and news text classification.

Description

technical field [0001] The invention belongs to the technical field of natural language processing, and in particular relates to a method for classifying short texts at the word and sentence levels based on deep learning. Background technique [0002] With the development of computer data processing technology, text classification technology has gradually matured and been widely used. Its applicable fields include sentiment analysis, topic classification, spam detection, etc. The development of deep learning technology has gradually highlighted the two important artificial neural network branches of convolutional neural network and recurrent neural network. Therefore, the convolutional neural network can be better applied to the field of computer vision by using its characteristics of extracting local features and effectively reducing weight parameters; the cyclic neural network has a strong memory and association ability for front and rear inputs, and is good at dealing wit...

AI Technical Summary

Problems solved by technology

In fact, many studies have ignored the combination of the sentence layer and the word layer, and feature compounding for complex feature extraction

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