Exploring the use of automatic linguistic analysis to improve standards development

The draft Standards in progress, when accompanied by reviewers' comments, represent a clear example of annotated textual data and are therefore attractive for "supervised machine learning" analyses. This article describes an ongoing experiment, in which a collection of annotated drafts is analyzed through automatic linguistic analysis techniques using various methods to identify effective solutions for improving the Standards creation process through the capability to predict the parts of the standard that are prone to be commented. The results achieved so far are presented and discussed along with new possible directions to take.