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
How to design a connectionist holistic parser
Neural Computation
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
Towards history-based grammars: using richer models for probabilistic parsing
ACL '93 Proceedings of the 31st annual meeting on Association for Computational Linguistics
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
Immediate-head parsing for language models
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
Training connectionist models for the structured language model
EMNLP '03 Proceedings of the 2003 conference on Empirical methods in natural language processing
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We present a neural-network-based statistical parser, trained and tested on the Penn Treebank. The neural network is used to estimate the parameters of a generative model of left-corner parsing, and these parameters are used to search for the most probable parse. The parser's performance (88.8% F-measure) is within 1% of the best current parsers for this task, despite using a small vocabulary size (512 inputs). Crucial to this success is the neural network architecture's ability to induce a finite representation of the unbounded parse history, and the biasing of this induction in a linguistically appropriate way.