Head-driven statistical models for natural language parsing
Head-driven statistical models for natural language parsing
PCFG models of linguistic tree representations
Computational Linguistics
Using an annotated corpus as a stochastic grammar
EACL '93 Proceedings of the sixth conference on European chapter of the Association for Computational Linguistics
Parsing algorithms and metrics
ACL '96 Proceedings of the 34th annual meeting on Association for Computational Linguistics
Parsing with the shortest derivation
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 1
Probabilistic tree-adjoining grammar as a framework for statistical natural language processing
COLING '92 Proceedings of the 14th conference on Computational linguistics - Volume 2
What is the minimal set of fragments that achieves maximal parse accuracy?
ACL '01 Proceedings of the 39th Annual Meeting on Association for Computational Linguistics
Accurate unlexicalized parsing
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
Tree-gram parsing lexical dependencies and structural relations
ACL '00 Proceedings of the 38th Annual Meeting on Association for Computational Linguistics
What to do when lexicalization fails: parsing German with suffix analysis and smoothing
ACL '05 Proceedings of the 43rd 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
Multilevel coarse-to-fine PCFG parsing
HLT-NAACL '06 Proceedings of the main conference on Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics
Bayesian learning of a tree substitution grammar
ACLShort '09 Proceedings of the ACL-IJCNLP 2009 Conference Short Papers
Simple, accurate parsing with an all-fragments grammar
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Blocked inference in Bayesian tree substitution grammars
ACLShort '10 Proceedings of the ACL 2010 Conference Short Papers
Empiricist solutions to nativist puzzles by means of unsupervised TSG
Proceedings of the Workshop on Computational Models of Language Acquisition and Loss
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We investigate full-scale shortest-derivation parsing (SDP), wherein the parser selects an analysis built from the fewest number of training fragments. Shortest derivation parsing exhibits an unusual range of behaviors. At one extreme, in the fully unpruned case, it is neither fast nor accurate. At the other extreme, when pruned with a coarse unlexicalized PCFG, the shortest derivation criterion becomes both fast and surprisingly effective, rivaling more complex weighted-fragment approaches. Our analysis includes an investigation of tie-breaking and associated dynamic programs. At its best, our parser achieves an accuracy of 87% F1 on the English WSJ task with minimal annotation, and 90% F1 with richer annotation.