Grammar specialization through entropy thresholds

Samuelsson, Christer

doi:10.3115/981732.981758

Cited by 14 publications

(17 citation statements)

References 7 publications

(9 reference statements)

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“…There, the basic idea is to learn special grammar rules from the original ones and a set of training examples by chunking together the former based on how they are used to parse the latter. The relevant references are (Samuelsson & Rayner 1991), (Samuelsson 1994a) and .…”

Section: Discussionmentioning

confidence: 99%

Example-based optimisation of surface-generation tables

Samuelsson¹

1997

Recent Advances in Natural Language Processing

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A method is given that \inverts" a logic grammar and displays it from the point of view of the logical form, rather than from that of the word string. LR-compiling techniques are used to allow a recursivedescent generation algorithm to perform \functor merging" much in the same way as an LR parser performs pre x merging. This is an improvement on the semantic-head-driven generator that results in a much smaller search space. The amount of semantic lookahead can be varied, and appropriate tradeo points between table size and resulting nondeterminism can be found automatically. This can be done by removing all spurious nondeterminism for input su ciently close to the examples of a training corpus, and large portions of it for other input, while preserving completeness. 1 1 I wish to thank greatly Gregor Erbach, Jussi Karlgren, Manny Rayner, Hans Uszkoreit, Mats Wir en and the anonymous reviewers of ACL, EACL, IJCAI and RANLP for valuable feedback on previous versions of this article. Special credit is due to Kristina Striegnitz, who assisted with the implementation.Parts of this article have previously appeared as (Samuelsson 1995). The presented work was funded by the N3 \Bidirektionale Linguistische Deduktion (BiLD)" project in the Sonderforschungsbereich 314 K unstliche Intelligenz | Wissensbasierte Systeme.

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Section: Discussionmentioning

confidence: 99%

Example-based optimisation of surface-generation tables

Samuelsson¹

1997

Recent Advances in Natural Language Processing

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“…Our pruning strategies are extremely simple. The cutting criteria employed in grammar specialization either require carefully manually tuning, or require more complicated statistical techniques (Samuelsson, 1994); automatically derived cutting criteria, however, perform considerably worse.…”

Section: Discussionmentioning

confidence: 99%

Learning efficient parsing

Noord

2009

Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics on - EACL '09

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A corpus-based technique is described to improve the efficiency of wide-coverage high-accuracy parsers. By keeping track of the derivation steps which lead to the best parse for a very large collection of sentences, the parser learns which parse steps can be filtered without significant loss in parsing accuracy, but with an important increase in parsing efficiency. An interesting characteristic of our approach is that it is self-learning, in the sense that it uses unannotated corpora.

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“…Subsequent work (Rayner and Carter, 1996;Samuelsson, 1994) views the problem as that of cutting up each tree in a treebank of correct parse trees into subtrees, after which the rule combinations corresponding to the subtrees determine the rules of the specialized grammar. This approach reports experimental results, using the SRI Core Language Engine, (Alshawi, 1992), in the ATIS domain, of more than a 3-fold speedup at a cost of 5% in grammatical coverage, the latter which is compensated by an increase in parsing accuracy.…”

Section: Introductionmentioning

confidence: 99%

“…This approach reports experimental results, using the SRI Core Language Engine, (Alshawi, 1992), in the ATIS domain, of more than a 3-fold speedup at a cost of 5% in grammatical coverage, the latter which is compensated by an increase in parsing accuracy. Later work (Samuelsson, 1994;Sima'an, 1999) attempts to automatically determine appropriate tree-cutting criteria, the former using local measures, the latter using global ones.…”

Section: Introductionmentioning

confidence: 99%

Corpus-based grammar specialization

Cancedda

Samuelsson

2000

Proceedings of the 2nd Workshop on Learning Language in Logic and the 4th Conference on Computational Natural Language Learning

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Broad-coverage grammars tend to be highly ambiguous. When such grammars are used in a restricted domain, it may be desirable to specialize them, in effect trading some coverage for a reduction in ambiguity. Grammar specialization is here given a novel formulation as an optimization problem, in which the search is guided by a global measure combining coverage, ambiguity and grammar size. The method, applicable to any unification grammar with a phrasestructure backbone, is shown to be effective in specializing a broad-coverage LFG for French.

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Grammar specialization through entropy thresholds

Cited by 14 publications

References 7 publications

Example-based optimisation of surface-generation tables

Example-based optimisation of surface-generation tables

Learning efficient parsing

Corpus-based grammar specialization

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