Notes on LR parser design

Samuelsson, Christer

doi:10.3115/991886.991953

Cited by 3 publications

(1 citation statement)

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“…Given a sufficiently large corpus parsed by the original, general, grammar, it is possible to identify the common combinations of grammar rules and "chunk" them into "macro-rules". The result is a "specialized" grammar; this has a larger number of rules, but a simpler structure, allowing it in practice to be parsed very much more quickly using an LR-based method [23]. The coverage of the specialized grammar is a strict subset of that of the original grammar; thus any analysis produced by the specialized grammar is guaranteed to be valid in the original one as well.…”

Section: Linguistically Motivated Robustmentioning

confidence: 99%

Hybrid language processing in the Spoken Language Translator

Rayner¹,

Carter

1997 IEEE International Conference on Acoustics, Speech, and Signal Processing

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The paper presents an overview of the Spoken Language Translator (SLT) system's hybrid language-processing architecture, focussing on the way in which rule-based and statistical methods are combined to achieve robust and efficient performance within a linguistically motivated framework. In general, we argue that rules are desirable in order to encode domain-independent linguistic constraints and achieve high-quality grammatical output, while corpus-derived statistics are needed if systems are to be efficient and robust; further, that hybrid architectures are superior from the point of view of portability to architectures which only make use of one type of information. We address the topics of "multi-engine" strategies for robust translation; robust bottom-up parsing using pruning and grammar specialization; rational development of linguistic rule-sets using balanced domain corpora; and efficient supervised training by interactive disambiguation. All work described is fully implemented in the current version of the SLT-2 system.

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Section: Linguistically Motivated Robustmentioning

confidence: 99%

Hybrid language processing in the Spoken Language Translator

Rayner¹,

Carter

1997 IEEE International Conference on Acoustics, Speech, and Signal Processing

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Grammar specialization through entropy thresholds

Samuelsson

1994

Proceedings of the 32nd Annual Meeting on Association for Computational Linguistics -

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Explanation-based generalization is used to extract a specialized grammar from the original one using a training corpus of parse trees. This allows very much faster parsing and gives a lower error rate, at the price of a small loss in coverage. Previously, it has been necessary to specify the tree-cutting criteria (or operationality criteria) manually; here they are derived automatically from the training set and the desired coverage of the specialized grammar. This is done by assigning an entropy value to each node in the parse trees and cutting in the nodes with sufficiently high entropy values.

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Fast parsing using pruning and grammar specialization

Rayner¹,

Carter²

1996

Proceedings of the 34th Annual Meeting on Association for Computational Linguistics -

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We show how a general grammar may be automatically adapted for fast parsing of utterances from a specific domain by means of constituent pruning and grammar specialization based on explanation-based learning. These methods together give an order of magnitude increase in speed, and the coverage loss entailed by grammar specialization is reduced to approximately half that reported in previous work. Experiments described here suggest that the loss of coverage has been reduced to the point where it no longer causes significant performance degradation in the context of a real application.

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Notes on LR parser design

Cited by 3 publications

References 5 publications

Hybrid language processing in the Spoken Language Translator

Hybrid language processing in the Spoken Language Translator

Grammar specialization through entropy thresholds

Fast parsing using pruning and grammar specialization

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