Inconsistency Detection in Semantic Annotations
Inconsistencies are part of any manually annotated corpus. Automatically finding these inconsistencies and correcting them (even manually) can increase the quality of the data. Past research has focused mainly on detecting inconsistency in syntactic annotation. This work explores new approaches to detecting inconsistency in semantic annotation. Two ranking methods are presented in this paper: a discrepancy ranking and an entropy ranking.
Possible applications of methods for inconsistency detection are improving the annotation procedure and guidelines, as well as correcting errors in completed annotations.
This project includes Python scripts for each ranking method and result data from testing the methods on various corpora (see LREC paper for further details).
The complete work can be found in my master thesis: http://project-archive.inf.ed.ac.uk/msc/20150152/msc_proj.pdf