A Convolution Neural Network Based Nursing-care Text Classification Model with a New Filter for Expressing Dependency Relations of Words
A Convolution Neural Network Based Nursing-care Text Classification Model with a New Filter for Expressing Dependency Relations of Words
复制标题
基于卷积神经网络的护理文本分类模型和表达词依存关系的新型过滤器
DOI：
10.1109/smc.2018.00156
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
Sakashita R.
中科院分区：
文献类型：
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作者：
Nii M.;Tsuchida Y.;Kato Y.;Uchinuno A.;Sakashita R.
提出了一种基于卷积神经网络的文本分类方法。CNN在计算机视觉和语音识别应用中表现出很强的性能。近年来，在一些研究中，CNN已被应用于句子分类应用。目前，为了提高护理质量，我们对护理文本分类进行了研究。在我们以前的工作中，已经提出了几种类型的特征定义，并用一些分类模型进行了检验，比如支持向量机。本文采用单层细胞神经网络对护理文本进行分类。每个护理文本被表示为一个串联的单词向量。每个单词被表示为由word2vec[1]-[4]获得的固定长度的单词向量。然后，使用基于二维CNN的分类方法对护理文本进行分类。所提出的CNN有一种新的过滤器来提取词之间的依存关系。从实验结果来看，本文提出的基于CNN的方法比我们以前的工作取得了更好的性能。
In this paper, a convolution neural network (CNN) based text classification method is proposed. CNNs show strong performance for computer vision and speech recognition applications. Recently, in some researches, CNNs have been applied to sentence classification applications. Currently, we have studied nursing-care text classification to improve nursing-care quality in Japan. In our former works, several types of feature definitions have been proposed and examined by some classification models like SVMs. In this paper, a single layer CNN is used for classifying nursing-care texts. Each nursing-care text is represented as a concatenated word vectors. Each word is represented as a fixed length word vector which is obtained by the word2vec [1]-[4]. Then, nursing-care texts are classified using a two-dimensional CNN-based classification method. The proposed CNN has a new kind of filters which extracts dependency relation between words. From our experimental results, the proposed CNN-based method obtained better performance than our former works.