Head-driven statistical models for natural language parsing
Head-driven statistical models for natural language parsing
A maximum-entropy-inspired parser
NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
An automatic treebank conversion algorithm for corpus sharing
ACL '94 Proceedings of the 32nd annual meeting on Association for Computational Linguistics
A statistical parser for Czech
ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
Building a large-scale annotated Chinese corpus
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
Scaling to very very large corpora for natural language disambiguation
ACL '01 Proceedings of the 39th Annual Meeting on Association for Computational Linguistics
Learning accurate, compact, and interpretable tree annotation
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Label correspondence learning for part-of-speech annotation transformation
Proceedings of the 18th ACM conference on Information and knowledge management
Exploiting heterogeneous treebanks for parsing
ACL '09 Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 1 - Volume 1
K-best combination of syntactic parsers
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 3 - Volume 3
Better automatic treebank conversion using a feature-based approach
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: short papers - Volume 2
Automatic Treebank Conversion via Informed Decoding - A Case Study on Chinese Treebanks
ACM Transactions on Asian Language Information Processing (TALIP)
| Hi-index | 0.00 |
In this paper, we focus on the challenge of automatically converting a constituency treebank (source treebank) to fit the standard of another constituency treebank (target treebank). We formalize the conversion problem as an informed decoding procedure: information from original annotations in a source treebank is incorporated into the decoding phase of a parser trained on a target treebank during the parser assigning parse trees to sentences in the source treebank. Experiments on two Chinese treebanks show significant improvements in conversion accuracy over baseline systems, especially when training data used for building the parser is small in size.