Head-driven statistical models for natural language parsing
Head-driven statistical models for natural language parsing
Building a large annotated corpus of English: the penn treebank
Computational Linguistics - Special issue on using large corpora: II
Three new probabilistic models for dependency parsing: an exploration
COLING '96 Proceedings of the 16th conference on Computational linguistics - Volume 1
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Discriminative learning and spanning tree algorithms for dependency parsing
Discriminative learning and spanning tree algorithms for dependency parsing
CoNLL-X shared task on multilingual dependency parsing
CoNLL-X '06 Proceedings of the Tenth Conference on Computational Natural Language Learning
Unsupervised methods for head assignments
EACL '09 Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics
A probabilistic generative model for an intermediate constituency-dependency representation
ACLstudent '10 Proceedings of the ACL 2010 Student Research Workshop
Third-order variational reranking on packed-shared dependency forests
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
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We propose a framework for dependency parsing based on a combination of discriminative and generative models. We use a discriminative model to obtain a k-best list of candidate parses, and subsequently rerank those candidates using a generative model. We show how this approach allows us to evaluate a variety of generative models, without needing different parser implementations. Moreover, we present empirical results that show a small improvement over state-of-the-art dependency parsing of English sentences.