Distributed Representations, Simple Recurrent Networks, And Grammatical Structure
Machine Learning - Connectionist approaches to language learning
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
Inducing history representations for broad coverage statistical parsing
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
How to Design a Connectionist Holistic Parser
Neural Computation
Deterministic left corner parsing
SWAT '70 Proceedings of the 11th Annual Symposium on Switching and Automata Theory (swat 1970)
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 general framework for adaptive processing of data structures
IEEE Transactions on Neural Networks
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We present a neural network based natural language parser. Training the neural network induces hidden representations of unbounded partial parse histories, which are used to estimate probabilities for parser decisions. This induction process is given domain-specific biases by matching the flow of information in the network to structural locality in the parse tree, without imposing any independence assumptions. The parser achieves performance on the benchmark datasets which is roughly equivalent to the best current parsers.