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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. . | From detecting errors to automatically correcting them Markus Dickinson Department of Linguistics Georgetown University mad87@georgetown.edu Abstract 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. 1 Introduction Annotated corpora serve as training material and as gold standard testing material for the development of tools in computational linguistics and as a source of data for theoretical linguists searching for relevant language patterns. However they contain annotation errors and such errors provide unreliable training and evaluation data as has been previously shown see ch. 1 of Dickinson 2005 and references therein . Improving the quality of linguistic annotation where possible is thus a key issue for the use of annotated corpora in computational and theoretical linguistics. Research has gone into automatically detecting annotation errors for part-of-speech annotation van Halteren 2000 Kveton and Oliva 2002 Dickinson and Meurers 2003 yet there has been virtually no work on automatically or semi-automatically correcting such annotation errors.1 1Oliva 2001 specifies hand-written rules to detect and Automatic correction can speed up corpus improvement efforts and provide new data for NLP technology training on the corpus. Additionally an investigation into automatic correction forces us to re-evaluate the technology using the corpus providing new insights into such technology. We propose in this paper to automatically correct .
