Nursing-care text evaluation using word vector representations realized by word2vec
Nursing-care text evaluation using word vector representations realized by word2vec
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DOI：
10.1109/fuzz-ieee.2016.7737960
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发表时间：
2016-07
期刊：
影响因子：
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通讯作者：
M. Nii;Yuya Tuchida;T. Iwamoto;A. Uchinuno;R. Sakashita
中科院分区：
文献类型：
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作者：
M. Nii;Yuya Tuchida;T. Iwamoto;A. Uchinuno;R. Sakashita
在本文中，我们讨论了一种使用 word2vec 的护理文本分类方法。 word2vec是一个提供连续词袋和skip-gram实现来实现词向量的工具。几年来，我们致力于对护理文本（即自由式日语文本）进行分类，以提高护理质量。多种机器学习方法已用于对此类文本进行分类。为了训练机器学习方法，我们使用了包含训练数据中出现的单词的单词列表。由于单词列表只是一个列表，没有考虑单词之间的关系。列表的长度也取决于单词的数量。词向量表示实现了任意维空间中的词表示。在本文中，我们使用 word2vec 作为替代单词列表。我们提出了一种基于文本中的依赖结构的新特征向量定义。根据实验结果，我们将提出的定义与我们之前的工作进行了比较。
In this paper, we discuss a classification method of nursing-care texts using the word2vec. The word2vec is a tool which provides the continuous bag-of-words and skip-gram implementations for realizing word vectors. We have tackled to classify nursing-care texts, which are freestyle Japanese texts, for improving nursing quality in several years. Several machine learning methods have been used for classifying such texts. To train a machine learning method, we used a word list which contains words appeared in the training data. Since the word list is a mere list, the relation among words is not considered. Also the length of the list depends on the number of words. Word vector representation realized word representations in arbitrary dimensional space. We use the word2vec as a alternative word list in this paper. And we propose a new feature vector definition which is based on dependency structures in a text. From experimental results, we compare the proposed definition with our previous works.