N12-1029 Correction With a competitive comma restoration system in place , we turn to
N03-1029 have a substantive advantage for comma restoration . In order to use more information
N03-1029 which we evaluate our alternative comma restoration models . Beeferman et al. present
N03-1029 provides significant improvement to comma restoration performance . As it turns out
N03-1029 at an opportunity for improved comma restoration . The ec distribution is especially
N12-1029 error correction as essentially a comma restoration task , we can we use largely
N12-1029 While the task remains similar to comma restoration , error correction in student
N03-1029 our extensions . The input to comma restoration is a sentence x = x1 ... xn of
N12-1029 results . 6 Annotation For the comma restoration task , we needed only to obtain
N12-1029 with the same tagger used in the comma restoration experiments . Because we approach
N03-1029 approach Treebank quality . As such , comma restoration may stand as the first end-user
N03-1029 significant performance improvement in comma restoration . ( Figure 1 lists performance
N03-1029 Section 23 . Precision of the comma restoration was 71.1 % and recall 55.2 %
N03-1029 5 Related Work We compare our comma restoration methods to those of Beeferman
N12-1029 error correction as we did for comma restoration . We still employ CRFs and label
N12-1029 state-of-the-art performance on the task of comma restoration , beating previous systems '
N12-1029 whereas they were removed in the comma restoration task . For error cor - rection
N12-1029 both the error correction and comma restoration tasks . We also developed and
N12-1029 single pass , rather than just comma restoration , but do provide results based
N12-1029 which has proven to be useful in comma restoration tasks ( see e.g. Lu and Ng ,
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