Multilayer feedforward networks are universal approximators
Neural Networks
Learning to Parse Natural Language with Maximum Entropy Models
Machine Learning - Special issue on natural language learning
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Incremental Syntactic Parsing of Natural Language Corpora with Simple Synchrony Networks
IEEE Transactions on Knowledge and Data Engineering
Discriminative Reranking for Natural Language Parsing
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
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
PCFG models of linguistic tree representations
Computational Linguistics
A maximum-entropy-inspired parser
NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
Efficient probabilistic top-down and left-corner parsing
ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
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
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Statistical parsing with a context-free grammar and word statistics
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
A Neural Syntactic Language Model
Machine Learning
Discriminative Reranking for Natural Language Parsing
Computational Linguistics
Discriminative training of a neural network statistical parser
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Probabilistic CFG with latent annotations
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Data-defined kernels for parse reranking derived from probabilistic models
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Fully parsing the Penn Treebank
HLT-NAACL '06 Proceedings of the main conference on Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics
Incremental Bayesian networks for structure prediction
Proceedings of the 24th international conference on Machine learning
Unlexicalised hidden variable models of split dependency grammars
HLT-Short '08 Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics on Human Language Technologies: Short Papers
Porting statistical parsers with data-defined kernels
CoNLL-X '06 Proceedings of the Tenth Conference on Computational Natural Language Learning
Semantic parsing for high-precision semantic role labelling
CoNLL '08 Proceedings of the Twelfth Conference on Computational Natural Language Learning
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
Loss minimization in parse reranking
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
Lookahead in deterministic left-corner parsing
IncrementParsing '04 Proceedings of the Workshop on Incremental Parsing: Bringing Engineering and Cognition Together
Accurate parsing of the proposition bank
NAACL-Short '06 Proceedings of the Human Language Technology Conference of the NAACL, Companion Volume: Short Papers
Domain adaptation with artificial data for semantic parsing of speech
NAACL-Short '09 Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Companion Volume: Short Papers
A latent variable model for generative dependency parsing
IWPT '07 Proceedings of the 10th International Conference on Parsing Technologies
A robust and hybrid deep-linguistic theory applied to large-scale parsing
ROMAND '04 Proceedings of the 3rd Workshop on RObust Methods in Analysis of Natural Language Data
Lexical and structural biases for function parsing
Parsing '05 Proceedings of the Ninth International Workshop on Parsing Technology
Constituent parsing by classification
Parsing '05 Proceedings of the Ninth International Workshop on Parsing Technology
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 1 - Volume 1
Structural bias in inducing representations for probabilistic natural language parsing
ICANN/ICONIP'03 Proceedings of the 2003 joint international conference on Artificial neural networks and neural information processing
Top-down nearly-context-sensitive parsing
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
Class-based approach to disambiguating levin verbs
Natural Language Engineering
Incremental Sigmoid Belief Networks for Grammar Learning
The Journal of Machine Learning Research
Bayesian network automata for modelling unbounded structures
IWPT '11 Proceedings of the 12th International Conference on Parsing Technologies
EMNLP-CoNLL '12 Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning
Multilingual joint parsing of syntactic and semantic dependencies with a latent variable model
Computational Linguistics
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We present a neural network method for inducing representations of parse histories and using these history representations to estimate the probabilities needed by a statistical left-corner parser. The resulting statistical parser achieves performance (89.1% F-measure) on the Penn Treebank which is only 0.6% below the best current parser for this task, despite using a smaller vocabulary size and less prior linguistic knowledge. Crucial to this success is the use of structurally determined soft biases in inducing the representation of the parse history, and no use of hard independence assumptions.