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- 2018
基于卷积神经网络的中文财经新闻分类方法
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Abstract:
摘要： 针对目前财经领域内新闻数据杂乱无章、缺乏自动高效管理等问题,提出一种基于卷积神经网络的中文财经新闻分类方法。收集大规模财经新闻语料,通过无监督学习方法训练获得一个广义通用的财经类词向量模型,将词向量引入到卷积神经网络模型训练中实现有效分类。与传统方法相比,基于卷积神经网络的中文财经新闻分类方法网络模型结构简单,针对小样本集也能表现优异的性能,不仅能有效解决中文财经新闻分类问题,还可充分证明卷积神经网络在处理文本分类问题中的有效性。
Abstract: In order to complete the task of financial news classification, a new method based on convolutional neural network for the classification of Chinese financial news was presented. A simple CNN was trained with one layer of convolution on top of word vectors obtained from an unsupervised neural language model. These vectors were trained on a large number of financial news corpus. Compared with the traditional methods, the network model based on convolutional neural network was simple in structure, which could show excellent performance by using small sample set. The method not only could solve the Chinese financial news classification problem effectively, but also prove the effectiveness of convolutional neural network in dealing with problems of text classification fully
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