Design And Implementation Of Text Data Enhancement System Based On Generative Adversarial Network

With the rapid development of artificial intelligence,computer technologies such as natural language processing have received great attention and have strong research potential.Text generation is of great significance in the field of natural language processing.High-quality text has an important impact on tasks related to natural language processing such as dialogue systems,intelligent translation,literary writing,and knowledge maps.Text data is prone to problems such as small number of samples,poor quality,and missing samples due to its discreteness and complexity of the semantic structure itself.The existing text generation technology research has made great progress,but there are still some limitations.For example,the method based on recurrent neural network uses explicit modeling of the data probability distribution and uses maximum likelihood estimation for training optimization,but the probability approximation of complex data such as text is complicated to calculate,and the probability distribution of the training data is overly dependent;the method based on convolutional neural network greatly reduces the number of parameters that need to be optimized due to the parameter sharing characteristics,but the effect of the network learning the long-distance dependency between words is poor;the method based on generative adversarial network uses the discriminator to measure the pros and cons of the mapping distribution,but there is still a problem that the signal sent back by the discriminator to the generator is not strong,which leads to the problem of unclear model training direction.Aiming at the above problems,this paper proposes a text data enhancement algorithm based on the generative adversarial network.First of all,aiming at the lack of feedback guidance signals in generator training,this paper uses the method of adding the feature guiding network to improve,and the higher-order text features extracted from the discriminator are transformed into the generator for feedback guidance.Secondly,in order to solve the problems of insufficient sampling and poor quality of generated text in the text generation process,this paper formulates relevant rules for text semantics,limits the time step of generation,and improves the quality of text generation.Finally,this paper conducts text generation comparison experiments on synthetic data,COCO text annotation,Chinese ancient poetry and other data sets.The results show that the FGGAN model proposed in this paper performs better than other comparison models on various indicators.Aiming at the proposed text data enhancement model based on generative adversarial network,this paper uses python language and TensorFlow framework to build and implement a data enhancement system suitable for text.It mainly includes text data preprocessing module,text enhancement task management module,text data enhancement module and text generation display module.Through functional and performance tests,it is verified that the text data enhancement system designed and implemented in this paper can improve the problem of small number of samples and poor quality of the data set,and can effectively support natural language processing tasks.