.png){kind=icon}
返回
Autoencoder for words
.png){kind=icon}
PRE
AI
DOI：10.1016/j.neucom.2013.09.055.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
This paper presents a training method that encodes each word into a different vector in semantic space and its relation to low entropy coding. Elman network is employed in the method to process word sequences from literary works. The trained codes possess reduced entropy and are used in ranking, indexing, and categorizing literary works. A modification of the method to train the multi-vector for each polysemous word is also presented where each vector represents a different meaning of its word. These multiple vectors can accommodate several different meanings of their word. This method is applied to the stylish analyses of two Chinese novels, Dream of the Red Chamber and Romance of the Three Kingdoms. (C) 2014 Elsevier B.V. All rights reserved.
Keyword:
Minimum entropy coding
Writing style similarity
Elman network
Polysemous word
Semantic indexing
期刊
IF:
6.5
论文数:
2.5W
被引数:
6.5W
机构
查看更多机构
引用论文
.png){kind=icon}
Phase Equilibria of Semiclathrate Hydrates of CO2, N2, CH4, or H2+ Tetra-n-butylammonium Bromide Aqueous SolutionPhase Equilibria of Semiclathrate Hydrates of CO2, N2, CH4,或H2 四-n-丁基溴化铵水溶液
PREAI
分享收藏分享收藏 没有更多内容