Automatic text generation method based on deep learning

Abstract

The invention discloses an automatic text generation method based on deep learning. The method includes a stage of obtaining a text generation model and a stage of calling the text generation model. The first stage includes the steps of preprocessing data, constructing a deep learning algorithm model, training a deep learning model and obtaining the text generation model. The second stage includesthe steps of accepting text input by a user, extracting feature information of the text input by the user, calling the text generation model and generating text matched with the feature information of the text input by the user. The first stage adopts the deep learning algorithm model to make the training process more automated, redundant manual intervention is eliminated, and a series of training strategies are used in the training process to make the text generated by the text generation model more readable. In the second stage, the user input information is classified, the intention of theuser is identified, and the text desired by the user is generated according to the intention of the user. The method is relatively easy to implement and has high applicability and great application especially in the aspect of article generation.

Description

technical field [0001] The invention belongs to the technical field of computer natural language processing, and in particular relates to an automatic text generation method based on deep learning. Background technique [0002] Deep learning has made a breakthrough in recent artificial intelligence research. It has ended the ten-year failure of artificial intelligence to make a breakthrough, and it has rapidly exerted influence in the industry. Deep learning is different from narrow artificial intelligence systems that can only complete specific tasks (functional simulation for specific tasks). As a general artificial intelligence technology, it can deal with various situations and problems. It has been obtained in the fields of computer vision, speech recognition, etc. The application of extremely fruitful results has also achieved certain results in the field of natural language processing. Deep learning is the most effective and effective method of implementing artificia...

AI Technical Summary

Problems solved by technology

This SMT machine learning method has the following disadvantages: 1) The generation of the next sentence only depends on the information of the previous sentence, which cannot guarantee the integrity of the generated text, that is, it is highly dependent on the local information in the text, and does not consider the global information of the input sentence. Comprehensive; 2) It is the mapping probability between words, which has poor modeling ability in semantics, and is often only applied in the case of equal or similar semantic information, that is, it only considers the information of words. The semantic considerations are very incomplete, resulting in chaotic and inconsistent semantics of the generated text

The traditional NN has the following disadvantages: 1) In the traditional NN model training process, too much attention is paid to semantic information; 2) Each word generated only considers the same global information

As a result, the generated text is single and tends to be biased in the wrong direction, that is, it cannot be adjusted according to the generated information, making the topic prone to deviation

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