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Probabilistic parsing strategies

descriptionPublicationkeyboard_double_arrow_right Article , Preprint , Conference object 01 Jan 2004Embargo end date: 01 Jan 2002 Italy, Netherlands Publisher:Association for Computational Linguistics (ACL)Journal:Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics - ACL '04

Authors: NEDERHOF M. J.; SATTA, GIORGIO;

doi: 10.3115/1218955.1219024 , 10.1145/1147954.1147959 , 10.48550/arxiv.cs/0211017

arXiv: http://arxiv.org/abs/cs/0211017

handle: 11370/42745ec9-a1d4-489e-9cc2-a5a537ca5de3 , 11577/1370736 , 11577/1370748 , 11577/1565582 , 11577/1370740

Probabilistic parsing strategies

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Abstract

We present new results on the relation between purely symbolic context-free parsing strategies and their probabilistic counterparts. Such parsing strategies are seen as constructions of push-down devices from grammars. We show that preservation of probability distribution is possible under two conditions, viz. the correct-prefix property and the property of strong predictiveness. These results generalize existing results in the literature that were obtained by considering parsing strategies in isolation. From our general results, we also derive negative results on so-called generalized LR parsing.

Countries

Italy, Netherlands

Related Organizations

University of Padua
Italy
University of Groningen
Netherlands

Keywords

FOS: Computer and information sciences, Computer Science - Computation and Language, I.2.7, push-down automata, Parsing algorithms; context-free grammars; probabilistic parsing; push-down automata; transduction, algorithms, transduction, probabilistic parsing, F.4.3, parsing algorithms, context-free grammars, LANGUAGES, AUTOMATA, theory, F.4.3; I.2.7, Computation and Language (cs.CL), FORM, CONTEXT-FREE GRAMMARS

Impact byBIP!

	citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	14
	popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.	Average
	influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	Top 10%
	impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.	Average