Paper: From Detecting Errors To Automatically Correcting Them

ACL ID E06-1034
Title From Detecting Errors To Automatically Correcting Them
Venue Annual Meeting of The European Chapter of The Association of Computational Linguistics
Session Main Conference
Year 2006
Authors

Faced with the problem of annotation er- rors in part-of-speech (POS) annotated corpora, we develop a method for auto- matically correcting such errors. Build- ing on top of a successful error detection method, wefirsttry correcting acorpus us- ing two off-the-shelf POS taggers, based on the idea that they enforce consistency; with this, we find some improvement. Af- ter some discussion of the tagging process, we alter the tagging model to better ac- count for problematic tagging distinctions. This modification results in significantly improved performance, reducing the error rate of the corpus.