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Analysis of classification results for the nursing-care text evaluation using convolutional neural networks

机译:基于卷积神经网络的护理文本评估分类结果分析

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摘要

In this paper, a convolutional neural network (CNN) based classification method is proposed. In computer vision and speech recognition areas, CNNs have obtained strong performance. Recently, CNNs have been applied to sentence classification. We have studied nursing-care text classification [5]-[17] for improving nursing-care quality. In our former works, several types of feature definitions were proposed and examined by some classification models. In this paper, a CNN is used for classification of nursing-care texts and then we analyze the trained CNN for extracting important part for decision of classification. First, each nursing-care text is represented as a concatenated word vectors. Then, every nursing-care text is classified using CNN-based classification methods. Next, we examined the structure of the trained CNN for extracting important parts of the nursing-care texts. From our experimental results, the proposed CNN-based method obtained better performance than our former works. And also the results suggest that the extracted part of each nursing-care text has importance for deciding its quality of nursing.
机译:本文提出了一种基于卷积神经网络的分类方法。在计算机视觉和语音识别领域,CNN表现出色。最近,CNN已应用于句子分类。我们研究了护理文本分类[5]-[17],以提高护理质量。在我们以前的工作中,提出了几种类型的特征定义,并通过一些分类模型进行了检验。本文使用CNN对护理文本进行分类,然后对经过训练的CNN进行分析,以提取重要的分类决策依据。首先,每个护理文本都表示为连接的单词向量。然后,使用基于CNN的分类方法对每个护理文本进行分类。接下来,我们检查了经过训练的CNN的结构,以提取护理文本的重要部分。从我们的实验结果来看,提出的基于CNN的方法比我们以前的工作获得了更好的性能。结果还表明,每个护理文本的提取部分对于决定其护理质量都具有重要意义。

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