Word complexity is defined in a number of different ways. Psycholinguistic,
morphological and lexical proxies are often used. Human ratings are also used.
The problem here is that these proxies do not measure complexity directly, and
human ratings are subject to subjective bias. In this study we contend that
some form of 'latent complexity' can be approximated by using samples of simple
and complex words. We use a sample of 'simple' words from primary school
picture books and a sample of 'complex' words from high school and academic
settings. In order to analyse the differences between these classes, we look at
the letter positional probabilities (LPPs). We find a strong statistical
association between simple and complex words on the basis of LPPs. For example,
simple words are significantly (p<.001) more likely to start with w, b, s, h,
g,k, j,t y or f, while complex words are significantly (p<.001) more likely to
start with i, a, e, r, v, u or d. We find similar strong associations for
subsequent letter positions, with 84 letter-position variables in the first 6
positions being significant at the p<.001 level. We then use LPPs as variables
in creating a classifier which can classify the two classes with an 83%
accuracy. We test these findings using a second data set, with 66 LPPs
significant (p<.001) in the first 6 positions common to both datasets. We use
these 66 variables to create a classifier that is able to classify a third
dataset with an accuracy of 70%. Finally, we create a fourth sample by
combining the extreme high and low scoring words generated by three classifiers
built on the first three separate datasets and use this sample to build a
classifier which has an accuracy of 97%. We use this to score the four levels
of English word groups from an ESL program.

通过使用简单和复杂词汇的样本，研究发现字母位置概率与简单和复杂词汇之间存在强大的统计关联，并创建一个基于字母位置概率的分类器，可以以 83% 的准确率对这两类进行分类。通过对额外数据集的测试，证实了这一发现，最终建立了一个准确率为 97% 的分类器，并用于评分英语课程中的四个级别的单词组。