Head-driven statistical models for natural language parsing
Head-driven statistical models for natural language parsing
New developments in parsing technology
Probabilistic CFG with latent annotations
ACL '05 Proceedings of the 43rd 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
Scalable inference and training of context-rich syntactic translation models
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
A best-first probabilistic shift-reduce parser
COLING-ACL '06 Proceedings of the COLING/ACL on Main conference poster sessions
Projective dependency parsing with perceptron
CoNLL-X '06 Proceedings of the Tenth Conference on Computational Natural Language Learning
Parsing '05 Proceedings of the Ninth International Workshop on Parsing Technology
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We try to improve the classifier-based deterministic dependency parsing in two ways: by introducing a better search method based on a non-deterministic nbest algorithm and by devising a series of linguistically richer models. It is experimentally shown on a ConLL 2007 shared task that this results in a system with higher performance while still keeping it simple enough for an efficient implementation.