返回
Characterizing linguistic structure with mutual information
DOI:10.1348/000712606X122760.png)
摘要
En 中文
We explore mutual information (MI) as a means of characterizing linguistic statistical structure. The MI between two linguistic tokens x and y is the degree to which seeing x helps us anticipate the occurrence of y. We computed MI between words in 595 samples of written text in 25 languages. Our analyses indicate that MI dependencies do not extend beyond a range of five words. Moreover, the similarity between MI profiles of different languages was used to cluster the languages. These results are discussed in terms of a putative link between short-term memory and linguistic structure and the further utility of MI in terms of characterizing the latter.
Keyword:
SHORT-TERM-MEMORY
MAGICAL NUMBER 7
WORKING-MEMORY
NARROW WINDOW
WORD-LENGTH
LANGUAGE
ACQUISITION
PREDICTION
NETWORKS
CAPACITY
AI总结
对已上传原文的论文进行重点信息的提取,主要内容包括:简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3.3
论文数:
2.1K
被引数:
5.7K
机构
暂无机构信息
引用论文
A flexible, highly conductive, tough ionogel electrolyte containing LiTFSI salt and ionic liquid [EMIM][TFSI] based on PVDF-HFP for high-performance supercapacitors一种基于pvdf-hfp的包含LiTFSI盐和离子液体 [EMIM][TFSI] 的柔性,高导电,韧性离子凝胶电解质,用于高性能超级电容器
Polymer
IF0

