Dynamically Jointing character and word embedding for Chinese text Classification

Xuetao Tang, Xuegang Hu, Peipei Li
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Abstract

Chinese text classification is drawing attention in these few years. Different from English texts, there is no natural separator between Chinese words. With the development of deep learning, many character-level only models have been proposed for Chinese text classification to tackle this problem, which have achieved more success than word-level models. But the word information is also important for Chinese text representation, especially for short texts with less information. However, most of neural network models either just concatenate character-level representation and word-level representation, or use massive external knowledge to represent the whole text, which is complex and time-consuming. For better and easier representing the Chinese text without any external knowledge and using as much character and word information as possible, we propose a simple model jointed character and word embedding dynamically, called DJCW. Firstly, the character-level and word-level BiLSTM Model is introduced to extract features of texts with indefinite lengths. Secondly, the char and word are weightedly combined and the weights are changed dynamically. Finally, experiments conducted on five open-source text datasets show our model can handle the texts with different lengths and has achieved good stability results.
动态连接字符和词嵌入的中文文本分类
近年来,中文文本分类越来越受到人们的关注。与英语文本不同,汉语单词之间没有自然的分隔符。随着深度学习的发展,人们提出了许多纯字符级的中文文本分类模型来解决这一问题,这些模型比词级模型取得了更大的成功。但是单词信息对于中文文本表示也很重要,特别是对于信息较少的短文本。然而,大多数神经网络模型要么只是将字符级表示和词级表示连接起来,要么使用大量的外部知识来表示整个文本,这既复杂又耗时。为了在不需要任何外部知识的情况下更好、更容易地表示中文文本,并尽可能多地使用字符和单词信息,我们提出了一个简单的动态连接字符和单词嵌入的模型,称为DJCW。首先,引入字符级和词级BiLSTM模型,提取不确定长度文本的特征;其次,对字符和单词进行加权组合,并动态改变权重;最后,在5个开源文本数据集上进行的实验表明,我们的模型可以处理不同长度的文本,并取得了良好的稳定性结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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