Correcting Errors in a Treebank Based on Tree Mining
Suzuki Kanta, Yoshihide Kato, Shigeki Matsubara · 2016
This paper provides a new method to correct annotation errors in a treebank.The previous error correction method constructs a pseudo parallel corpus where incorrect partial parse trees are paired with correct ones, and extracts error correction rules from the parallel corpus.By applying these rules to a treebank, the method corrects errors.However, this method does not achieve wide coverage of error correction.To achieve wide coverage, our method adopts a different approach.In our method, we consider that an infrequent pattern which can be transformed to a frequent one is an annotation error pattern.Based on a tree mining technique, our method seeks such infrequent tree patterns, and constructs error correction rules each of which consists of an infrequent pattern and a corresponding frequent pattern.We conducted an experiment using the Penn Treebank.We obtained 1,987 rules which are not constructed by the previous method, and the rules achieved good precision.