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Marketing text recognition method and system

A text recognition and text technology, applied in neural learning methods, character and pattern recognition, biological neural network models, etc., can solve problems such as not fully considering label semantics and overall label relevance

Pending Publication Date: 2022-07-08
UNIV OF JINAN
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, such methods assume that all labels are independent of each other, and do not fully consider label semantics and overall label correlation.

Method used

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  • Marketing text recognition method and system
  • Marketing text recognition method and system
  • Marketing text recognition method and system

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0052] This embodiment provides a marketing text recognition method. First, an independent text graph is constructed for each document, and a label graph is constructed by using the label information of the training set; then a topic model is trained by using the label information corresponding to the text graph nodes and the text graph, Generate the topic word probability distribution; then build a perceptron to map the topic word probability distribution to the label vector space, build a graph convolution network, learn the label correlation and semantic information under the guidance of the label graph, and update the label vector to obtain the label embedding representation; Finally, after jointly learning words and labels to obtain the final text representation, the final classification result is output through the sigmoid layer. like figure 1 shown, including the following steps:

[0053] Step 1: Obtain the training set and the text to be recognized, and perform data p...

Embodiment 2

[0121] This embodiment provides a marketing text recognition system, which specifically includes the following modules:

[0122] A preprocessing module, which is configured to: obtain the text to be recognized, and perform preprocessing;

[0123] The graph building module is configured to: construct a text graph of the to-be-recognized text based on the pre-processed to-be-recognized text;

[0124] a joint learning module, which is configured to: generate a text-level word representation based on a text graph of the text to be recognized, and combine the embedded representations of all tags to generate a text representation;

[0125]A classification module, which is configured to: based on the text representation, use a classifier to obtain a result of whether the text to be recognized belongs to the marketing text;

[0126] Among them, the method of obtaining the embedded representation of the label is: based on the text map of the training set and its labels, generate the p...

Embodiment 3

[0129] This embodiment provides a computer-readable storage medium on which a computer program is stored, and when the program is executed by a processor, implements the steps in the method for recognizing marketing text as described in the first embodiment above.

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Abstract

The invention provides a marketing text recognition method and system, and the method comprises the steps: obtaining a to-be-recognized text, and carrying out the preprocessing of the to-be-recognized text; based on the preprocessed to-be-recognized text, constructing a text graph of the to-be-recognized text; generating text-level word representation based on the text graph of the to-be-recognized text, and generating text representation in combination with the embedded representations of all the tags; based on the text representation, a classifier is adopted to obtain a result whether the to-be-recognized text belongs to the marketing text or not; wherein the obtaining method of the embedded representation of the labels comprises the steps of generating topic word probability distribution based on a text graph of a training set and the labels of the text graph, mapping the topic word probability distribution to a label vector space, and learning a correlation relation and semantic information between the labels under the guidance of a label graph to obtain the embedded representation of the labels. The purpose of generating complete label embedding is achieved, more information related to classification is captured by combining learning words and labels, and the precision of marketing text recognition is improved.

Description

technical field [0001] The invention belongs to the technical field of natural language processing, and in particular relates to a marketing text recognition method and system. Background technique [0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art. [0003] Every day, social media platforms publish a large number of marketing articles with advertising content for promotional purposes. In order to eliminate readers' disgust and win the trust of potential customers, marketing content is often hidden in ordinary article content, which is difficult to identify and discover. Different from traditional media, some self-media editors carefully design advertising content for profit, and even exaggerate facts and edit false information, which will not only mislead consumers and harm consumers' interests, but also damage a healthy network environment. Therefore, corresponding met...

Claims

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Application Information

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IPC IPC(8): G06V30/416G06V30/413G06N3/08G06N3/04G06K9/62G06F40/30G06V10/764G06V10/82
CPCG06F40/30G06N3/08G06N3/048G06N3/044G06N3/045G06F18/241G06F18/214
Inventor 马坤李乐平纪科陈贞翔杨波
Owner UNIV OF JINAN