Results 201 to 210 of about 42,602 (238)

Probabilistic Grammars for Natural Languages

Synthese, 1970
Although a fully adequate grammar for a substantial portion of any natural language does not exist, a vigorous and controversial discussion of how to choose among several competing grammars has already developed. On occasion, criteria of simplicity have been suggested as systematic scientific criteria for selection.
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Probabilistic grammar-based deep neuroevolution

Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2019
Designing deep neural networks by human engineers can be challenging because there are various choices of deep neural network structures. We developed a deep neuroevolution system to automatically design deep neural network structures using deep neuroevolution. Our approach defines a set of structures using a probabilistic grammar and searches for best
Pak-Kan Wong, Man-Leung Wong, Kwong-Sak Leung   +2 more
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Language Correction Using Probabilistic Grammars

IEEE Transactions on Computers, 1976
Error correction of programming languages has been effected in a heuristic fashion; error correction in the information-theoretic sense is very precise. The missing link is provided through probabilistic grammars. This paper provides the theoretical foundation for the precise construction of an error correcting compiler. The concept of code distance is
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A reestimation algorithm for probabilistic dependency grammars

Natural Language Engineering, 1999
A probabilistic parameter reestimation algorithm plays a key role in the automatic acquisition of stochastic grammars. In the case of context-free phrase structure grammars, the inside-outside algorithm is widely used. However, it is not directly applicable to Probabilistic Dependency Grammar (PDG), because PDG is not based on constituents but on a ...
Lee, SM LEE, SEUNGMI, Choi, KS Choi, Key-Sun   +1 more
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Probabilistic Feature Grammars

2000
We present a new formalism, probabilistic feature grammar (PFG). PFGs combine most of the best properties of several other formalisms, including those of Collins, Magerman, and Charniak, and in experiments have comparable or better performance. PFGs generate features one at a time, probabilistically, conditioning the probabilities of each feature on ...
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Probabilistic Parallel Communicating Grammar Systems

International Journal of Computer Mathematics, 2002
Grammar systems are theoretical models of distributed computing which play a major role in modern Computer Science. In this paper, we define and study a variant of Parallel Communicating(PC) grammar systems namely, Probabilistic PC grammar systems which serves as a grammatical model for random distributed processing.
K. Arthi, Kamala Krithivasan
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Data mining using Probabilistic Grammars

2016 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT), 2016
Efficient and accurate data mining has become vital as technology advancements in data collection and storage soar. Researchers have proposed various valuable machine learning algorithms for data mining. However, not many have utilized formal methods. This paper proposes a data mining approach using Probabilistic Context Free Grammars (PCFGs).
Aljoharah Algwaiz, Sanguthevar Rajasekaran, Reda Ammar   +2 more
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Efficient probabilistic grammar induction for design

Artificial Intelligence for Engineering Design, Analysis and Manufacturing, 2018
AbstractThe use of grammars in design and analysis has been set back by the lack of automated ways to induce them from arbitrarily structured datasets. Machine translation methods provide a construct for inducing grammars from coded data which have been extended to be used for design through pre-coded design data.
Mark E. Whiting, Jonathan Cagan, Philip R. Leduc   +2 more
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On probabilistic contextual grammars

Fundam. Informaticae, 2005
Summary: A generalization of contextual grammars by adding probabilities is introduced, enriching the generative power of such grammars. An example exhibits a non-contextual, even not context-free language.
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Learning restricted probabilistic link grammars

1996
We describe a language model employing a new headed-disjuncts formulation of Lafferty et al.'s (1992) probabilistic link grammar, together with (1) an EM training method for estimating the probabilities, and (2) a procedure for learning some simple lexicalized grammar structures.
Fong, Eva Wai-man, Wu, Dekai
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