Incremental generation of parsers

Heering, Jan; Klint, Paul; Rekers, J.G.

doi:10.1145/73141.74834

Cited by 23 publications

(11 citation statements)

References 4 publications

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“…We scanned and parsed 735 files of Python source code, 786,793 tokens in total; the results are shown in Table 2. There is the standard Earley parser, of course, and two versions of PEP: one which precomputes the split LR(0) -DFA and another which lazily constructs the state machine (similar to the approach taken in [24] for GLR parsing). All timings reported in this section include both the time to recognize the input and the time to construct a rightmost derivation.…”

Section: Resultsmentioning

confidence: 99%

Practical Earley Parsing

Aycock

Horspool²

2002

The Computer Journal

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Earley's parsing algorithm is a general algorithm, able to handle any context-free grammar. As with most parsing algorithms, however, the presence of grammar rules having empty right-hand sides complicates matters. By analyzing why Earley's algorithm struggles with these grammar rules, we have devised a simple solution to the problem. Our empty-rule solution leads to a new type of finite automaton expressly suited for use in Earley parsers and to a new statement of Earley's algorithm. We show that this new form of Earley parser is much more time efficient in practice than the original.

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Section: Resultsmentioning

confidence: 99%

Practical Earley Parsing

Aycock

Horspool²

2002

The Computer Journal

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“…Our on-demand optimization of a dynamic programming into an automaton is related to similar optimizations applied in other areas: regular expression matching, parsing [HKR90] and simulating outof-order processors [SL98].…”

Section: Related Workmentioning

confidence: 99%

Fast and flexible instruction selection with on-demand tree-parsing automata

2006

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Tree parsing as supported by code generator generators like BEG, burg, iburg, lburg and ml-burg is a popular instruction selection method. There are two existing approaches for implementing tree parsing: dynamic programming, and tree-parsing automata; each approach has its advantages and disadvantages. We propose a new implementation approach that combines the advantages of both existing approaches: we start out with dynamic programming at compile time, but at every step we generate a state for a tree-parsing automaton, which is used the next time a tree matching the state is found, turning the instruction selector into a fast tree-parsing automaton. We have implemented this approach in the Gforth code generator. The implementation required little effort and reduced the startup time of Gforth by up to a factor of 2.5.

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“…Fischer (1980) was first to describe a method for incrementally updating an LR(1) table. Heering et al(1990) use the principle of lazy evaluation to attack the same problem. Our design of incremental update LR is more closely related to ilalr for the following reasons:…”

Section: Incrementally Updating the Lr Tablementioning

confidence: 99%

An efficient algorithm to induce minimum average lookahead grammars for incremental LR parsing

Shen

2004

Proceedings of the Workshop on Incremental Parsing Bringing Engineering and Cognition Together - IncrementParsing '04

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We define a new learning task, minimum average lookahead grammar induction, with strong potential implications for incremental parsing in NLP and cognitive models. Our thesis is that a suitable learning bias for grammar induction is to minimize the degree of lookahead required, on the underlying tenet that language evolution drove grammars to be efficiently parsable in incremental fashion. The input to the task is an unannotated corpus, plus a nondeterministic constraining grammar that serves as an abstract model of environmental constraints confirming or rejecting potential parses. The constraining grammar typically allows ambiguity and is itself poorly suited for an incremental parsing model, since it gives rise to a high degree of nondeterminism in parsing. The learning task, then, is to induce a deterministic LR (k) grammar under which it is possible to incrementally construct one of the correct parses for each sentence in the corpus, such that the average degree of lookahead needed to do so is minimized. This is a significantly more difficult optimization problem than merely compiling LR (k) grammars, since k is not specified in advance. Clearly, naïve approaches to this optimization can easily be computationally infeasible. However, by making combined use of GLR ancestor tables and incremental LR table construction methods, we obtain an O(n 3 + 2 m) greedy approximation algorithm for this task that is quite efficient in practice.

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Incremental generation of parsers

Cited by 23 publications

References 4 publications

Practical Earley Parsing

Practical Earley Parsing

Fast and flexible instruction selection with on-demand tree-parsing automata

An efficient algorithm to induce minimum average lookahead grammars for incremental LR parsing

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