基本信息来源于合作网站,原文需代理用户跳转至来源网站获取       
摘要:
Statistics of languages are usually calculated by counting characters, words, sentences, word rankings. Some of these random variables are also the main “ingredients” of classical readability formulae. Revisiting the readability formula of Italian, known as GULPEASE, shows that of the two terms that determine the readability index G—the semantic index , proportional to the number of characters per word, and the syntactic index GF, proportional to the reciprocal of the number of words per sentence—GF is dominant because GC is, in practice, constant for any author throughout seven centuries of Italian Literature. Each author can modulate the length of sentences more freely than he can do with the length of words, and in different ways from author to author. For any author, any couple of text variables can be modelled by a linear relationship y = mx, but with different slope m from author to author, except for the relationship between characters and words, which is unique for all. The most important relationship found in the paper is that between the short-term memory capacity, described by Miller’s “7 ? 2 law” (i.e., the number of “chunks” that an average person can hold in the short-term memory ranges from 5 to 9), and the word interval, a new random variable defined as the average number of words between two successive punctuation marks. The word interval can be converted into a time interval through the average reading speed. The word interval spreads in the same range as Miller’s law, and the time interval is spread in the same range of short-term memory response times. The connection between the word interval (and time interval) and short-term memory appears, at least empirically, justified and natural, however, to be further investigated. Technical and scientific writings (papers, essays, etc.) ask more to their readers because words are on the average longer, the readability index G is lower, word and time intervals are longer. Future work done on ancient languages, such as the classical
推荐文章
一种基于long short-term memory的唇语识别方法
唇语识别
long short-term memory
计算机视觉
AMP吸收电厂烟气CO2性能的实验
电厂烟气
脱碳
AMP
吸收速率
应用Amp-FLP单体型连锁分析检测Wilson's患者的基因突变
肝豆状核变性
多态性,限制性片段长度
基因
突变
内容分析
关键词云
关键词热度
相关文献总数  
(/次)
(/年)
文献信息
篇名 Deep Language Statistics of Italian throughout Seven Centuries of Literature and Empirical Connections with Miller’s 7 ∓2 Law and Short-Term Memory
来源期刊 统计学期刊(英文) 学科 医学
关键词 GULPEASE ITALIAN LITERATURE Miller’s LAW READABILITY Short-Term Memory Word Interval
年,卷(期) 2019,(3) 所属期刊栏目
研究方向 页码范围 373-406
页数 34页 分类号 R73
字数 语种
DOI
五维指标
传播情况
(/次)
(/年)
引文网络
引文网络
二级参考文献  (0)
共引文献  (0)
参考文献  (0)
节点文献
引证文献  (0)
同被引文献  (0)
二级引证文献  (0)
2019(0)
  • 参考文献(0)
  • 二级参考文献(0)
  • 引证文献(0)
  • 二级引证文献(0)
研究主题发展历程
节点文献
GULPEASE
ITALIAN
LITERATURE
Miller’s
LAW
READABILITY
Short-Term
Memory
Word
Interval
研究起点
研究来源
研究分支
研究去脉
引文网络交叉学科
相关学者/机构
期刊影响力
统计学期刊(英文)
半月刊
2161-718X
武汉市江夏区汤逊湖北路38号光谷总部空间
出版文献量(篇)
584
总下载数(次)
0
总被引数(次)
0
论文1v1指导