Detecting errors in part-of-speech annotation

Abstract

We propose a new method for detecting errors in "gold-standard" part-of-speech annotation. The approach locates errors with high precision based on n-grams occurring in the corpus with multiple taggings. Two further techniques, closed-class analysis and finite-state tagging guide patterns, are discussed. The success of the three approaches is illustrated for the Wall Street Journal corpus as part of the Penn Tree-bank.