Correcting Syntactic Annotation Errors Based on Tree Mining
Summary :
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 if an infrequent pattern can be transformed to a frequent one, then it 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.
- Publication
- IEICE TRANSACTIONS on Information Vol.E100-D No.5 pp.1106-1113
- Publication Date
- 2017/05/01
- Publicized
- 2017/01/23
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2016EDP7357
- Type of Manuscript
- PAPER
- Category
- Natural Language Processing
Authors
Kanta SUZUKI
Nagoya University
Yoshihide KATO
Nagoya University
Shigeki MATSUBARA
Nagoya University
Keyword
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Kanta SUZUKI, Yoshihide KATO, Shigeki MATSUBARA, "Correcting Syntactic Annotation Errors Based on Tree Mining" in IEICE TRANSACTIONS on Information,
vol. E100-D, no. 5, pp. 1106-1113, May 2017, doi: 10.1587/transinf.2016EDP7357.
Abstract: 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 if an infrequent pattern can be transformed to a frequent one, then it 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.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2016EDP7357/_p
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@ARTICLE{e100-d_5_1106,
author={Kanta SUZUKI, Yoshihide KATO, Shigeki MATSUBARA, },
journal={IEICE TRANSACTIONS on Information},
title={Correcting Syntactic Annotation Errors Based on Tree Mining},
year={2017},
volume={E100-D},
number={5},
pages={1106-1113},
abstract={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 if an infrequent pattern can be transformed to a frequent one, then it 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.},
keywords={},
doi={10.1587/transinf.2016EDP7357},
ISSN={1745-1361},
month={May},}
Copy
TY - JOUR
TI - Correcting Syntactic Annotation Errors Based on Tree Mining
T2 - IEICE TRANSACTIONS on Information
SP - 1106
EP - 1113
AU - Kanta SUZUKI
AU - Yoshihide KATO
AU - Shigeki MATSUBARA
PY - 2017
DO - 10.1587/transinf.2016EDP7357
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E100-D
IS - 5
JA - IEICE TRANSACTIONS on Information
Y1 - May 2017
AB - 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 if an infrequent pattern can be transformed to a frequent one, then it 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.
ER -
English
