计算机应用研究2017，Vol.34Issue(4)：991-994,4.DOI:10.3969/j.issn.1001-3695.2017.04.007
基于事件卷积特征的新闻文本分类
Event convolutional feature based news documents classification
摘要
Abstract
In text modeling and classification,previous convolutional neural network (CNN) approaches processed on the ngram features based on the literal order of texts.They neglected the syntactic structure and semantic information over long distance dependencies.This paper proposed a event convolutional feature based model to overcome the defects by making use of semantic characteristics of events.It found events from text and applied a CNN to extract features for classification.In Chinese news multi-class classification experiment,the method performs better than traditional ones and is more balanced than n-gram CNN models.The experiment result shows the effectiveness of the model as well as the superiority of the event features.关键词
文本分类/事件/卷积神经网络/自然语言处理Key words
text classification/event/convolutional neural networks(CNN)/natural language processing (NLP)分类
信息技术与安全科学引用本文复制引用
夏从零,钱涛,姬东鸿..基于事件卷积特征的新闻文本分类[J].计算机应用研究,2017,34(4):991-994,4.基金项目
国家自然科学基金重点资助项目(61133012) （61133012）
