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TEXT QUALITY INSPECTION METHOD, ELECTRONIC APPARATUS, COMPUTER DEVICE AND STORAGE MEDIUM

机译:文本质量检查方法,电子设备,计算机设备和存储介质

摘要

Disclosed are a text quality inspection method, an electronic apparatus, a computer device and a storage medium. The method comprises: collecting a plurality of keywords of a WeChat text, and labelling the plurality of keywords to obtain a quality inspection text data set with a quality inspection label (301); constructing a neural network, and dividing, by means of the neural network, the quality inspection text data set into a training set and a verification set according to a fixed ratio (302); carrying out word segmentation on texts in the training set and the verification set by using a Chinese word segmentation tool to obtain a plurality of words, and mapping each word into a word vector (303); splitting the mapped training set into a plurality of training subsets, alternately training a plurality of quality inspection models by using the plurality of training subsets, and in the training process, saving a quality inspection model meeting a requirement from among the plurality of quality inspection models (304); and carrying out prediction by using the quality inspection model meeting a requirement, and re-checking a prediction result, wherein the prediction refers to using the saved quality inspection model to check the WeChat text (305). According to the text quality inspection method, the electronic apparatus, the computer device and the storage medium, there is a certain semantic comprehension capability, thereby improving the quality inspection accuracy rate, alleviating the pressure on quality inspection personnel, and greatly improving the text quality inspection efficiency.
机译:公开了文本质量检查方法,电子设备,计算机设备和存储介质。该方法包括:收集微信文本中的多个关键词,并对所述多个关键词进行标注,以获得带有质量检验标签的质量检验文本数据集(301);构造神经网络,并通过神经网络将质量检验文本数据集按照固定比例分为训练集和验证集(302);通过使用中文分词工具对训练集和验证集中的文本进行分词以获得多个词,并将每个词映射为词向量(303);将映射后的训练集划分为多个训练子集,通过使用多个训练子集交替训练多个质量检验模型,在训练过程中,从多个质量检验模型中保存满足要求的质量检验模型(304);通过使用满足要求的质量检查模型进行预测,并重新检查预测结果,其中,所述预测是指使用保存的质量检查模型检查微信文本(305)。根据文本质量检查方法,电子设备,计算机设备和存储介质,具有一定的语义理解能力,从而提高了质量检查的准确率,减轻了质量检查人员的压力,大大提高了文本质量检查效率。

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