Discriminative Reranking for Natural Language Parsing
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
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Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Statistical decision-tree models for parsing
ACL '95 Proceedings of the 33rd annual meeting on Association for Computational Linguistics
Incremental parsing with the perceptron algorithm
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Coarse-to-fine n-best parsing and MaxEnt discriminative reranking
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Efficient parsing of highly ambiguous context-free grammars with bit vectors
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
Learning to rank with SoftRank and Gaussian processes
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
A global joint model for semantic role labeling
Computational Linguistics
Search-based structured prediction
Machine Learning
Training parsers by inverse reinforcement learning
Machine Learning
Scalable discriminative parsing for German
IWPT '09 Proceedings of the 11th International Conference on Parsing Technologies
Learning to Rank for Information Retrieval and Natural Language Processing
Learning to Rank for Information Retrieval and Natural Language Processing
Third-order variational reranking on packed-shared dependency forests
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Features for phrase-structure reranking from dependency parses
IWPT '11 Proceedings of the 12th International Conference on Parsing Technologies
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We propose the subtree ranking approach to parse forest reranking which is a generalization of current perceptron-based reranking methods. For the training of the reranker, we extract competing local subtrees, hence the training instances (candidate subtree sets) are very similar to those used during beam-search parsing. This leads to better parameter optimization. Another chief advantage of the framework is that arbitrary learning to rank methods can be applied. We evaluated our reranking approach on German and English phrase structure parsing tasks and compared it to various state-of-the-art reranking approaches such as the perceptron-based forest reranker. The subtree ranking approach with a Maximum Entropy model significantly outperformed the other approaches.