A practical method for constructing LR (k) processors
Communications of the ACM
Context-Sensitive Statistics for Improved Grammatical Language Models
Context-Sensitive Statistics for Improved Grammatical Language Models
Tree-bank Grammars
PCFG models of linguistic tree representations
Computational Linguistics
Robust German noun chunking with a probabilistic context-free grammar
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 2
Learning grammars for different parsing tasks by partition search
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
ConLL '00 Proceedings of the 2nd workshop on Learning language in logic and the 4th conference on Computational natural language learning - Volume 7
Learning computational grammars
ConLL '01 Proceedings of the 2001 workshop on Computational Natural Language Learning - Volume 7
Learning grammars for different parsing tasks by partition search
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
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PCFG Learning by Partition Search is a general grammatical inference method for constructing, adapting and optimising PCFGS. Given a training corpus of examples from a language, a canonical grammar for the training corpus, and a parsing task, Partition Search PCFG Learning constructs a grammar that maximises performance on the parsing task and minimises grammar size. This paper describes Partition Search in detail, also providing theoretical background and a characterisation of the family of inference methods it belongs to. The paper also reports an example application to the task of building grammars for noun phrase extraction, a task that is crucial in many applications involving natural language processing. In the experiments, Partition Search improves parsing performance by up to 21.45% compared to a general baseline and by up to 3.48% compared to a task-specific baseline, while reducing grammar size by up to 17.25%.