We present an extension of the classic A* search procedure to tabular PCFG parsing. The use of A* search can dramatically reduce the time required to find a best parse by conservatively estimating the probabilities of parse completions. We discuss various estimates and give efficient algorithms for computing them. On average-length Penn treebank sentences, our most detailed estimate reduces the total number of edges processed to less than 3% of that required by exhaustive parsing, and a simpler estimate, which requires less than a minute of precomputation, reduces the work to less than 5%. Unlike best-first and finite-beam methods for achieving this kind of speed-up, an A* method is guaranteed to find the most likely parse, not just an approximation. Our parser

We show how techniques known from generalized LR parsing can be applied to leftcorner parsing. The esulting parsing algorithm for context-free grammars has some advantages over generalized LR parsing: the sizes and generation times of the parsers are smaller, the produced output is more compact, and the basic parsing technique can more easily be adapted to arbitrary context-free grammars.

We develop an improved form of left-corner chart parsing for large context-free grammars, introducing improvements that result in significant speed-ups compared to previously-known variants of left-corner parsing. We also compare our method to several other major parsing approaches, and find that our improved left-corner parsing method outperforms each of these across a range of grammars. Finally, we also describe a new technique for minimizing the extra information needed to efficiently recover parses from the data structures built in the course of parsing. 1

In this paper we relate a number of parsing algorithms which have been developed in very different areas of parsing theory, and which include deterministic algorithms, tabular algorithms, and a parallel algorithm. We show that these algorithms are based on the same underlying ideas.

This paper presents empirical studies and closely corresponding theoretical models of the performance of a chart parser exhaustively parsing the Penn Treebank with the Treebank's own CFG grammar. We show how performance is dramatically affected by rule representation and tree transformations, but little by top-down vs. bottom-up strategies.

We give a new treatment of tabular LR parsing, which is an alternative to Tomita's generalized LR algorithm. The advantage is twofold. Firstly, our treatment is conceptually more attractive because it uses simpler concepts, such as grammar trans- formations and standard tabulation techniques also know as chart parsing. Secondly, the static and dynamic complexity of parsing, both in space and time, is significantly reduced.

Deterministic parsing techniques are typically used in favor of general parsing algorithms for efficiency reasons. However, general algorithms such as Earley's method are more powerful and also easier for developers to use, because no seemingly arbitrary restrictions are placed on the grammar. We describe how to narrow the performance gap between general and deterministic parsers, constructing a directly executable Earley parser that can reach speeds comparable to deterministic methods even on grammars for commonly-used programming languages.

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Abstract − We provide a reformulation of Cocke−Younger−Kasami’s algorithm for recognizing context-free languages in which there are no references either to indices of table entries or to the length of the input string. Some top-down analogues of this functional approach are discussed as well.

This article presents a novel approach to empirical comparison between parsers for different grammar formalisms such as LTAG and HPSG. The key idea of our approach is to use strongly equivalent grammars obtained by grammar conversion, which generate equivalent parse results for the same input. We validate our approach by giving a formal proof of strong equivalence for an existing grammar conversion from LTAG to HPSG-style grammar. Experimental results using two pairs of LTAG and HPSG parsers with dynamic programming and CFG filtering reveal that the different ways of using the parsing techniques cause significant performance differences, and also suggest a definite way of improving these parsing techniques. RSUM. Nous prsentons une approche nouvelle pour comparer de manire empirique des analyseurs syntaxiques pour des formalismes grammaticaux comme LTAG et HPSG. L'ide centrale consiste utiliser des grammaires fortement quivalentes obtenues par conversion et produisant des analyses quivalentes pour la mme chane d'entre. Nous validons cette approche en fournissant une preuve formelle d'quivalence forte pour le mcanisme de conversion de grammaires de LTAG vers HPSG. Les rsultats exprimentaux obtenus avec deux paires d'analyseurs syntaxiques LTAG et HPSG en utilisant la programmation dynamique et le filtrage CFG ont montr que les diffrences de performance rsultent des diffrence d'adaptation des techniques d'analyse, suggrant une piste solide pour amliorer celles-ci.