Ambiguity packing in constraint-based parsing: practical results
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ACL '01 Proceedings of the 39th Annual Meeting on Association for Computational Linguistics
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ACL '01 Proceedings of the 39th Annual Meeting on Association for Computational Linguistics
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ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
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ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
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HLT '02 Proceedings of the second international conference on Human Language Technology Research
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COLING-ACL '06 Proceedings of the COLING/ACL on Interactive presentation sessions
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COLING-ACL '06 Proceedings of the COLING/ACL on Main conference poster sessions
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CICLing '07 Proceedings of the 8th International Conference on Computational Linguistics and Intelligent Text Processing
Semi-supervised training of a statistical parser from unlabeled partially-bracketed data
IWPT '07 Proceedings of the 10th International Conference on Parsing Technologies
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We present a novel approach for applying the Inside-Outside Algorithm to a packed parse forest produced by a unification-based parser. The approach allows a node in the forest to be assigned multiple inside and outside probabilities, enabling a set of 'weighted GRs' to be computed directly from the forest. The approach improves on previous work which either loses efficiency by unpacking the parse forest before extracting weighted GRs, or places extra constraints on which nodes can be packed, leading to less compact forests. Our experiments demonstrate substantial increases in parser accuracy and throughput for weighted GR output.