摘要
近年尽管针对中文本文分类的研究成果不少,但基于深度学习对中文政策等长文本进行自动分类的研究还不多见。为此,借鉴和拓展传统的数据增强方法,提出集成新时代人民日报分词语料库(NEPD)、简单数据增强(EDA)算法、word2vec和文本卷积神经网络(TextCNN)的NEWT新型计算框架;实证部分,基于中国地方政府发布的科技政策文本进行算法校验。实验结果显示,在取词长度分别为500、750和1000词的情况下,应用NEWT算法对中文科技政策文本进行分类的效果优于RCNN、Bi-LSTM和CapsNet等传统深度学习模型,F1值的平均提升比例超过13%;同时,NEWT在较短取词长度下能够实现全文输入的近似效果,可以部分改善传统深度学习模型在中文长文本自动分类任务中的计算效率。
In recent years,although there are many research outputs on the classification of Chinese text,there are still very few publications involving automatic classification of Chinese policy texts based on deep learning.Based on the current studies,a new computing framework-NEWT is proposed,which integrates NEPD(New Era People’s Daily Segmented Corpus),EDA(Easy Data Augmentation),Word2Vec and TextCNN.In the empirical analysis,the text of science and technology policy of Chinese local government is extracted,and the classification experiment is conducted.The experimental results show that the NEWT algorithm is better than the traditional deep learning models such as RCNN,Bi-LSTM and CapsNet when the length of words is 500,750 and 1000,respectively,the average increase ratio of F1 value is more than 13%.At the same time,NEWT can achieve the approximate effect of full-text input under a relatively short word length,which can partially improve the computational efficiency of the traditional deep learning model in the task of automatic classification of Chinese long text.
作者
李牧南
王良
赖华鹏
Li Munan;Wang Liang;Lai Huapeng(School of Business Administration,South China University of Technology,Guangzhou 510641,China;Guangdong Key Laboratory on Innovation Methods&Decision Management Systems,Guangzhou 510641,China)
出处
《科技管理研究》
CSSCI
北大核心
2023年第2期160-166,共7页
Science and Technology Management Research
基金
国家自然科学基金面上项目“基于多源数据融合与机器学习的新兴技术风险挖掘研究”(72074081)
广东省自然科学基金面上项目“关键共性技术识别及其演化趋势研究:多源数据融合与知识图谱视角”(2020A151501438)
关键词
NEWT
深度学习
数据增强
卷积神经网络
政策文本分类
中文长文本
NEWT
deep learning
data augmentation
convolutional neural networks
policy-text classification
long-length text in Chinese
