Weighted Deductive Parsing and Knuth's Algorithm

We present an algorithm for incremental statistical parsing with Parallel Multiple Context-Free Grammars (PMCFG). This is an extension of the algorithm by Angelov (2009) to which we added statistical ranking. We show that the new algorithm is several times faster than other statistical PMCFG parsing algorithms on real-sized grammars. At the same time the algorithm is more general since it supports non-binarized and non-linear grammars.We also show that if we make the search heuristics non-admissible, the parsing speed improves even further, at the risk of returning sub-optimal solutions.

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“…We define the algorithm as weighted deduction system (Nederhof, 2003) which generalizes Angelov's system.…”

Section: Deduction Systemmentioning

confidence: 99%

Fast Statistical Parsing with Parallel Multiple Context-Free Grammars

Angelov¹,

Ljunglöf²

2014

Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics

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“…This is known as uniform-cost search or shortest-hyperpath search (Nederhof, 2003). We halt as soon as a full parse (the special accept item) pops from the agenda, since uniform-cost search (as a special case of the A * algorithm) guarantees this to be the maximum-probability parse.…”

Section: Probabilistic Parsingmentioning

confidence: 99%

Favor Short Dependencies: Parsing with Soft and Hard Constraints on Dependency Length

Eisner

Smith

2010

Text, Speech and Language Technology

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In lexicalized phrase-structure or dependency parses, a word's modifiers tend to fall near it in the string. This fact can be exploited by parsers. We first show that a crude way to use dependency length as a parsing feature can substantially improve parsing speed and accuracy in English and Chinese, with more mixed results on German. We then show similar improvements by imposing hard bounds on dependency length and (additionally) modeling the resulting sequence of parse fragments. The approach with hard bounds, "vine grammar," accepts only a regular language, even though it happily retains a context-free parameterization and defines meaningful parse trees. We show how to parse this language in O(n) time, using a novel chart parsing algorithm with a low grammar constant (rather than an impractically large finite-state recognizer with an exponential grammar constant).

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“…The problem of finding the most probable derivation is discussed by Knuth (1977) and Nederhof (2003) in the general case, and by Jelinek et al (1992) for grammars in Chomsky normal form. The problem of finding the most probable string is discussed by Paz (1971), Casacuberta and de la Higuera (2000), Sima'an (2002) and Blondel and Canterini (2003).…”

Section: Further Informationmentioning

confidence: 99%