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

A technology of deep learning and classification methods, applied in the field of short text classification based on deep learning

Pending Publication Date: 2020-01-21
HARBIN ENG UNIV
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  • Description
  • Claims
  • Application Information

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

Method used

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

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Embodiment Construction

[0028] Below in conjunction with the content of the invention, a detailed implementation and effect of the present invention will be described through the following examples.

[0029]A short text classification method based on deep learning-level convolutional neural network for processing short text classification tasks. The core of the present invention is based on the word vector technology, realize the sentence vector by connecting multiple sets of feature maps obtained by convolving multiple convolution kernels in the convolutional neural network to convolute the word vector in the sentence, and then retain the sentence layer structure of the text to complete the sentence content expression. A sentence is a structure that carries words, so the essence of a sentence is still a word. After the two-dimensional matrix composed of word vectors is subjected to one-dimensional convolution and maximum pooling with n convolution kernels, an n-dimensional vector composed of multip...

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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...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F40/289G06F40/30G06F16/35G06K9/62G06N3/04G06N3/08
CPCG06F16/35G06N3/08G06N3/044G06N3/045G06F18/24
Inventor 杨悦孟宪禹
Owner HARBIN ENG UNIV
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