  Dictionaries are often developed using tools that save to Extensible Markup
Language (XML)-based standards. These standards often allow high-level
repeating elements to represent lexical entries, and utilize descendants of
these repeating elements to represent the structure within each lexical entry,
in the form of an XML tree. In many cases, dictionaries are published that have
errors and inconsistencies that are expensive to find manually. This paper
discusses a method for dictionary writers to quickly audit structural
regularity across entries in a dictionary by using statistical language
modeling. The approach learns the patterns of XML nodes that could occur within
an XML tree, and then calculates the probability of each XML tree in the
dictionary against these patterns to look for entries that diverge from the
norm.
