计算机应用研究2018，Vol.35Issue(2)：396-399,4.DOI:10.3969/j.issn.1001-3695.2018.02.017
中文语义组块自动抽取方法
Research on automatic extraction of Chinese semantic clustering unit
摘要
Abstract
Sentence semantic representation is not only a key problem in natural language processing to be solved at present,but also an important restriction factor whether nature language processing is ability to make deep application.Based on the characteristics of the Chinese text,this paper abandoned the point of separating semantic and grammar,and then put forward a new style of semantic clustering unit,and did a research on information extraction with a deep learning model based on deep belief net,the model took the noun as the core in the sentence and combined the noun with its before and after words to from semantic clustering unit.Then,it used three extraction methods,neural network,support vector machine and depth belief network,to construct the extraction model.Experimentally,there are three groups of experimental,finally results show that under the conditions of large data,deep belief network methods compare with support vector machines and neural networks,which has better effect.关键词
语义表述/深度信念网络/深度学习/中文语义组块Key words
semantic representation/deep belief net/deep learning/Chinese semantic clustering unit分类
信息技术与安全科学引用本文复制引用
钟茂生,荆佳琦..中文语义组块自动抽取方法[J].计算机应用研究,2018,35(2):396-399,4.基金项目
国家自然科学基金资助项目(61462027,61363072) （61462027,61363072）
