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 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.- Anthology ID:
- L16-1244
- Volume:
- Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)
- Month:
- May
- Year:
- 2016
- Address:
- Portorož, Slovenia
- Venue:
- LREC
- SIG:
- Publisher:
- European Language Resources Association (ELRA)
- Note:
- Pages:
- 1540–1545
- Language:
- URL:
- https://aclanthology.org/L16-1244
- DOI:
- Cite (ACL):
- Kanta Suzuki, Yoshihide Kato, and Shigeki Matsubara. 2016. Correcting Errors in a Treebank Based on Tree Mining. In Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16), pages 1540–1545, Portorož, Slovenia. European Language Resources Association (ELRA).
- Cite (Informal):
- Correcting Errors in a Treebank Based on Tree Mining (Suzuki et al., LREC 2016)
- PDF:
- https://preview.aclanthology.org/paclic-22-ingestion/L16-1244.pdf
- Data
- Penn Treebank