  Recent publications suggest using natural language analysis on database
schema elements to guide tuning and profiling efforts. The underlying
hypothesis is that state-of-the-art language processing methods, so-called
language models, are able to extract information on data properties from schema
text.
  This paper examines that hypothesis in the context of data correlation
analysis: is it possible to find column pairs with correlated data by analyzing
their names via language models? First, the paper introduces a novel benchmark
for data correlation analysis, created by analyzing thousands of Kaggle data
sets (and available for download). Second, it uses that data to study the
ability of language models to predict correlation, based on column names. The
analysis covers different language models, various correlation metrics, and a
multitude of accuracy metrics. It pinpoints factors that contribute to
successful predictions, such as the length of column names as well as the ratio
of words. Finally, \rev{the study analyzes the impact of column types on
prediction performance.} The results show that schema text can be a useful
source of information and inform future research efforts, targeted at
NLP-enhanced database tuning and data profiling.
