CASIA OpenIR  > 模式识别国家重点实验室  > 自然语言处理
王少楠1; 宗成庆1,2
Source Publication计算机学报
Other Abstract
Semantic memory is the foundation of human language understanding. Human brain needs to encode, retrieve and decode word meanings for language understanding. The semantic representation is the key step to develop natural language processing systems. Some studies have shown that the formation of concepts is affected by the interaction of human brain and the real world, and the concepts in human brain contain rich forms of information including vision, perception and language. Based on the distributional hypothesis which states that “similar words occur in similar contexts”, the concepts are represented as vectors by calculating the co-occurrence frequency of each word and its statistical features. In this way, word representation in computer can be seen as the semantic representation in human brain. This article mainly focuses on how to represent word senses and do word senses induction in natural language text. We first investigate the relation between computational models of word representation and semantic representation in human brain. Based on word similarity experiments, we have verified that word representations by statistical methods can capture the relationship of similarity between words in human brain. 
Keyword词义表示 词义归纳 词义消歧 主题模型 双通道主题模型
WOS Keyword词义表示 ;  词义归纳 ;  词义消歧 ;  主题模型 ; 双通道主题模型
Document Type期刊论文
Recommended Citation
GB/T 7714
王少楠,宗成庆. 一种基于双通道LDA模型的汉语词义表示与归纳方法[J]. 计算机学报,2016,39(8):1652-1666.
APA 王少楠,&宗成庆.(2016).一种基于双通道LDA模型的汉语词义表示与归纳方法.计算机学报,39(8),1652-1666.
MLA 王少楠,et al."一种基于双通道LDA模型的汉语词义表示与归纳方法".计算机学报 39.8(2016):1652-1666.
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