The possibilities of automatic detection/correction of errors in tagged corpora: A pilot study on a German corpus

Abstract

The performance of taggers is usually evaluated by their percentual success rate. Because of the pure quantitativity of such an approach, all errors committed by the tagger are treated on a par for the purpose of the evaluation. This paper takes a different, qualitative stand on the topic, arguing that the previous viewpoint is not linguistically adequate: the errors (might) differ in severity. General implications for tagging are discussed, and a simple method is proposed and exemplified, able to 1. detect and in some cases even rectify the most severe errors and thus 2. contribute to arriving finally at a better tagged corpus. Some encouraging results achieved by a very simple, manually performed test and evaluation on a small sample of a corpus are given.