Paper Info
Title
Inconsistency Detection in Semantic Annotation.
Abstract
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. Those methods are then tested and evaluated on multiple corpora annotated with multiword expressions and supersense labels. The results show considerable improvements in detecting inconsistency candidates over a random baseline. Possible applications of methods for inconsistency detection are improving the annotation procedure and guidelines, as well as correcting errors in completed annotations.
Year
Venue
Keywords
2016
LREC 2016 - TENTH INTERNATIONAL CONFERENCE ON LANGUAGE RESOURCES AND EVALUATION
inconsistency detection,annotation,error detection,corpora
Field
DocType
Citations 
Semantic annotation,Information retrieval,Computer science,Natural language processing,Artificial intelligence
Conference
4
PageRank 
References 
Authors
0.40
13
3
Name
Order
Citations
PageRank
Nora Hollenstein151.78
Nathan Schneider2131970.25
Bonnie Lynn Webber31511317.14