From Detecting Errors to Automatically Correcting Them

Faced with the problem of annotation errors in part-of-speech (POS) annotated corpora, we develop a method for automatically correcting such errors. Building on top of a successful error detection method, we first try correcting a corpus using two off-the-shelf POS taggers, based on the idea that they enforce consistency; with this, we find some improvement. After some discussion of the tagging process, we alter the tagging model to better account for problematic tagging distinctions. This modification results in significantly improved performance, reducing the error rate of the corpus.

• Type: Conference
• Year: 2006
• Where: EACL
• Authors: Markus Dickinson
• Keywords: EACL 2006, Error Detection Method, Natural Language Processing, Off-the-shelf Pos Taggers, Problematic Tagging Distinctions