A DOP model for semantic interpretation

Bonnema, Remko; Bod, Rens; Scha, Remko

doi:10.3115/976909.979638

Cited by 30 publications

(34 citation statements)

References 8 publications

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“…Note that the subtree probabilities in Likelihood-DOP are directly estimated from their relative frequencies in the treebank-trees. While the relative-frequency estimator obtains competitive results on several domains (Bonnema et al 1997;Bod, 2001a;De Pauw, 2000), it does not maximize the likelihood of the training data (Johnson, 2002). This is because there may be hidden derivations which the relative-frequency estimator cannot deal with.…”

Section: Likelihood-dopmentioning

confidence: 95%

A Unified Model of Structural Organization in Language and Music

Bod¹

2002

jair

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Is there a general model that can predict the perceived phrase structure in language and music? While it is usually assumed that humans have separate faculties for language and music, this work focuses on the commonalities rather than on the differences between these modalities, aiming at finding a deeper "faculty". Our key idea is that the perceptual system strives for the simplest structure (the "simplicity principle"), but in doing so it is biased by the likelihood of previous structures (the "likelihood principle"). We present a series of dataoriented parsing (DOP) models that combine these two principles and that are tested on the Penn Treebank and the Essen Folksong Collection. Our experiments show that (1) a combination of the two principles outperforms the use of either of them, and (2) exactly the same model with the same parameter setting achieves maximum accuracy for both language and music. We argue that our results suggest an interesting parallel between linguistic and musical structuring.

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Section: Likelihood-dopmentioning

confidence: 95%

A Unified Model of Structural Organization in Language and Music

Bod¹

2002

jair

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“…Accuracies on the Homecentre Tables 3 and 4 show that there is a consistent increase in parse accuracy for all metrics if larger fragments are included, but that the increase itself decreases. This phenomenon is also known as the DOP hypothesis (Bod 1998), and has been confirmed for Tree-DOP on the ATIS, OVIS and Wall Street Journal treebanks (see Bod 1993Bod , 1998Bod , 1999Bod , 2000aSima'an 1999;Bonnema et al 1997;Hoogweg 2000). The current result thus extends the validity of the DOP hypothesis to LFG annotations.…”

Section: Comparing Different Fragment Sizesmentioning

confidence: 96%

“…But the model has also been applied to several other grammatical frameworks, e.g. Tree-Insertion Grammar (Hoogweg 2000), Tree-Adjoining Grammar (Neumann 1998), Lexical-Functional Grammar (Bod & Kaplan 1998;Cormons 1999), Head-driven Phrase Structure Grammar (Neumann & Flickinger 1999), and Montague Grammar (Bonnema et al 1997;Bod 1999). Most probability models for DOP use the relative frequency estimator to estimate fragment probabilities, although Bod (2000b) trains fragment probabilities by a maximum likelihood reestimation procedure belonging to the class of expectation-maximization algorithms.…”

Section: Introductionmentioning

confidence: 99%

An improved parser for data-oriented lexical-functional analysis

Bod

2000

Proceedings of the 38th Annual Meeting on Association for Computational Linguistics - ACL '00

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We present an LFG-DOP parser which uses fragments from LFG-annotated sentences to parse new sentences. Experiments with the Verbmobil and Homecentre corpora show that (1) Viterbi n best search performs about 100 times faster than Monte Carlo search while both achieve the same accuracy; (2) the DOP hypothesis which states that parse accuracy increases with increasing fragment size is confirmed for LFG-DOP; (3) LFG-DOP's relative frequency estimator performs worse than a discounted frequency estimator; and (4) LFG-DOP significantly outperforms Tree-DOP if evaluated on tree structures only.

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“…Another approach within treebank parsing is Data-Oriented Parsing (DOP) (Bod 1992(Bod , 1993Bonnema, Bod and Scha 1997), which collects statistics on the occurrence frequency of all tree fragments within a corpus, derives any sentence by assembling such fragments, and scores any parse in terms of the sum of the probabilities of all 380 A. Krotov and others of its derivations (and hence this model presents significant problems in terms of computational overhead 3 ).…”

Section: Treebank Grammars and Parsingmentioning

confidence: 99%

Evaluating two methods for Treebank grammar compaction

Krotov

Hepple²,

Gaizauskas

et al. 1999

Nat. Lang. Eng.

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Treebanks, such as the Penn Treebank, provide a basis for the automatic creation of broad coverage grammars. In the simplest case, rules can simply be 'read off' the parse-annotations of the corpus, producing either a simple or probabilistic context-free grammar. Such grammars, however, can be very large, presenting problems for the subsequent computational costs of parsing under the grammar. In this paper, we explore ways by which a treebank grammar can be reduced in size or 'compacted', which involve the use of two kinds of technique: (i) thresholding of rules by their number of occurrences; and (ii) a method of rule-parsing, which has both probabilistic and non-probabilistic variants. Our results show that by a combined use of these two techniques, a probabilistic context-free grammar can be reduced in size by 62% without any loss in parsing performance, and by 71% to give a gain in recall, but some loss in precision.

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A DOP model for semantic interpretation

Cited by 30 publications

References 8 publications

A Unified Model of Structural Organization in Language and Music

A Unified Model of Structural Organization in Language and Music

An improved parser for data-oriented lexical-functional analysis

Evaluating two methods for Treebank grammar compaction

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