A noisy illegal short text recognition method based on a dual-channel text convolutional neural network
A convolutional neural network and recognition method technology, applied in the field of computer natural language processing, can solve the problems of variant feature identification of illegal users, difficulty in constructing variant features, etc., and achieve the effect of improving accuracy and robustness
- Summary
- Abstract
- Description
- Claims
- Application Information
AI Technical Summary
Problems solved by technology
Method used
Image
Examples
Embodiment Construction
[0023] The present invention will be described in detail below in conjunction with the accompanying drawings. It should be noted that the described embodiments are only for understanding of the present invention, and do not limit it in any way.
[0024] The method of the present invention is not limited to processing pornographic text information, and other similar illegal advertising information can also be effectively processed, such as: various invoiced advertising texts, only need to collect relevant sample information to obtain the corresponding recognizer through learning. In this embodiment, the main object of processing is pornographic promotional text, that is, to identify various pornographic advertisement text information released by various illegal users on the network platform, and most of these information have added noise to break through the existing illegal text information. Detection Systems. This embodiment is implemented using tensorflow, a deep learning fr...
PUM
Abstract
Description
Claims
Application Information
- R&D Engineer
- R&D Manager
- IP Professional
- Industry Leading Data Capabilities
- Powerful AI technology
- Patent DNA Extraction
Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic, Popular Technical Reports.
© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com