Relaxation and neural learning: points of convergence and divergence
Journal of Parallel and Distributed Computing - Neural Computing
Elements of information theory
Elements of information theory
Constraint Grammar: A Language-Independent System for Parsing Unrestricted Text
Constraint Grammar: A Language-Independent System for Parsing Unrestricted Text
ICG! '96 Proceedings of the 3rd International Colloquium on Grammatical Inference: Learning Syntax from Sentences
Optimization-based Heuristics for Maximal Constraint Satisfaction
CP '95 Proceedings of the First International Conference on Principles and Practice of Constraint Programming
Tagging accurately: don't guess if you know
ANLC '94 Proceedings of the fourth conference on Applied natural language processing
Tagging French: comparing a statistical and a constraint-based method
EACL '95 Proceedings of the seventh conference on European chapter of the Association for Computational Linguistics
Specifying a shallow grammatical representation for parsing purposes
EACL '95 Proceedings of the seventh conference on European chapter of the Association for Computational Linguistics
Acquiring disambiguation rules from text
ACL '89 Proceedings of the 27th annual meeting on Association for Computational Linguistics
Constraint grammar as a framework for parsing running text
COLING '90 Proceedings of the 13th conference on Computational linguistics - Volume 3
Part-of-speech tagging with neural networks
COLING '94 Proceedings of the 15th conference on Computational linguistics - Volume 1
Word-sense disambiguation using statistical models of Roget's categories trained on large corpora
COLING '92 Proceedings of the 14th conference on Computational linguistics - Volume 2
POS tagging using relaxation labelling
COLING '96 Proceedings of the 16th conference on Computational linguistics - Volume 2
A Machine Learning Approach to POS Tagging
Machine Learning
An Integrated Statistical Model for Tagging and Chunking Unrestricted Text
TDS '00 Proceedings of the Third International Workshop on Text, Speech and Dialogue
A shallow parser based on closed-class words to capture relations in biomedical text
Journal of Biomedical Informatics
Extracting molecular binding relationships from biomedical text
ANLC '00 Proceedings of the sixth conference on Applied natural language processing
A flexible POS tagger using an automatically acquired language model
ACL '98 Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics and Eighth Conference of the European Chapter of the Association for Computational Linguistics
Tagging and chunking with bigrams
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 2
Mapping WordNets using structural information
ACL '00 Proceedings of the 38th Annual Meeting on Association for Computational Linguistics
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We describe the use of energy function optimisation in very shallow syntactic parsing. The approach can use linguistic rules and corpus-based statistics, so the strengths of both linguistic and statistical approaches to NLP can be combined in a single framework. The rules are contextual constraints for resolving syntactic ambiguities expressed as alternative tags, and the statistical language model consists of corpus-based n-grams of syntactic tags. The success of the hybrid syntactic disambiguator is evaluated against a held-out benchmark corpus. Also the contributions of the linguistic and statistical language models to the hybrid model are estimated.