基于计算智能的文本数据识别模型设计

O. Dmitrieva, E. Babenko
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引用次数: 0

摘要

本文研究了一种基于混合结构的神经网络文本数据识别模型的开发、论证、软件实现和研究。所建议的模型允许您通过不同活动和文本分析方法的组合获得新知识。本工作的主要任务是研究文本数据识别的本质和相关性,确定识别质量评估的标准,设计模型的体系结构和软件应用,开发和训练文本识别的软件模型。在其他神经网络的参与下,进行了软件应用测试,并对识别质量的准确性、可靠性、完整性和时间进行了对比分析。使用损失函数作为评估神经网络训练质量的标准,它表征了神经网络得到的实际值的结果与预期值的归一化偏差。在训练和测试阶段,对模型进行调整,根据可靠性度量确定最优训练次数。基于所获得的结果,可以认为所提出的ConvBiGRU神经网络混合架构具有最高的质量指标,与ConvBiLSTM模型相似,但实现一个历元所需的时间更少。当处理大量文本数据时,与简单模型相比,组合模型的epoch实现时间也趋于平衡。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
DESIGNING A TEXT DATA RECOGNITION MODEL USING COMPUTATIONAL INTELLIGENCE
The article is devoted to the development, justification, software implementation and research of a text data recognition model based on the use of neural networks with a hybrid architecture. The proposed model allows you to gain new knowledge through a combination of different activities and methods of text analysis. The main tasks implemented in the work were to study the essence and relevance of text data recognition, determine the criteria for assessing the quality of recognition, design the architecture of the model and software application, develop and train a software model of text recognition. Software application testing and a comparative analysis of recognition quality in terms of accuracy, reliability, completeness and time were carried out with the involvement of other neural networks. As a criterion for assessing the quality of neural network training, the loss function was used, which characterized the normalized deviation of the results of the actual values obtained by the neural network from the expected ones. At the stages of training and testing, the model was tuned, the optimal number of training epochs was determined in terms of the reliability metric. Based on the results obtained, it can be argued that the proposed hybrid architecture of the ConvBiGRU neural network has the highest quality indicators, similar to the ConvBiLSTM model, but the time spent on the implementation of one epoch is less. There was also a tendency to equalize the times of realization of epochs for combined models compared to simple models when large arrays of text data were processed.
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