Incremental parsing by modular recurrent connectionist networks
Advances in neural information processing systems 2
The ATIS spoken language systems pilot corpus
HLT '90 Proceedings of the workshop on Speech and Natural Language
Connectionist Natural Language Processing: Readings from Connection Science
Connectionist Natural Language Processing: Readings from Connection Science
Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition
A Hybrid Approach to Natural Language Parsing
ICANN 96 Proceedings of the 1996 International Conference on Artificial Neural Networks
Deterministic parsing of syntactic non-fluencies
ACL '83 Proceedings of the 21st annual meeting on Association for Computational Linguistics
ACL '92 Proceedings of the 30th annual meeting on Association for Computational Linguistics
Journal of Artificial Intelligence Research
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The concept of Dynamic Neural Networks (DNN) is a new approach within the Neural Network paradigm, which is based on the dynamic construction of Neural Networks during the processing of an input. The DNN methodology has been employed in the Hybrid Connectionist Parsing (HCP) approach, which comprises an incremental, on-line generation of a Neural Network parse tree. The HCP ensures an adequate representation and processing of recursively defined structures, like grammar-based languages. In this paper, we describe the general principles of the HCP method and some of its specific Neural Network features. We outline and discuss the use of the HCP method with respect to parallel processing of ambiguous structures, and robust parsing of extra-grammatical inputs in the context of spoken language parsing.