Results 11 to 20 of about 1,215,272 (369)
الأستاذ, 2023 Current linguistic theory presumes languages to be essentially similar because individuals have a genetic inclination to acquire language. Linguists strive to create a model of this abstract universal grammar that captures the core commonalities among ...
Inst. Dr. Ahmed Hamid Abdulrazzaq
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Meaning and Formal Semantics in Generative Grammar [PDF]
, 2014 Stephen Schiffer
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Generative Adversarial Network Rooms in Generative Graph Grammar Dungeons for The Legend of Zelda [PDF]
IEEE Congress on Evolutionary Computation, 2020 Generative Adversarial Networks (GANs) have demonstrated their ability to learn patterns in data and produce new exemplars similar to, but different from, their training set in several domains, including video games.
Jake Gutierrez, Jacob Schrum
semanticscholar +1 more source
A Structural Grammar Approach for the Generative Design of Diagrid-Like Structures
, 2021 An innovative generative design strategy, based on shape grammar, is proposed for the minimum-weight design of diagrid tall buildings. By considering the building as a three-dimensional vertical cantilever beam with a tubular section under horizontal ...
F. Cascone +3 more
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Capacity Bounded Grammars and Petri Nets [PDF]
Electronic Proceedings in Theoretical Computer Science, 2009 A capacity bounded grammar is a grammar whose derivations are restricted by assigning a bound to the number of every nonterminal symbol in the sentential forms.
Ralf Stiebe, Sherzod Turaev
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Generalized multitext grammars [PDF]
Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics - ACL '04, 2004 Generalized Multitext Grammar (GMTG) is a synchronous grammar formalism that is weakly equivalent to Linear Context-Free Rewriting Systems (LCFRS), but retains much of the notational and intuitive simplicity of Context-Free Grammar (CFG). GMTG allows both synchronous and independent rewriting.
D. MELAMED +2 more
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Data-Efficient Graph Grammar Learning for Molecular Generation [PDF]
International Conference on Learning Representations, 2022 The problem of molecular generation has received significant attention recently. Existing methods are typically based on deep neural networks and require training on large datasets with tens of thousands of samples.
Minghao Guo +5 more
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Bulgarian polemics on American Generativism 1950s-1970s: a peek through the Iron curtain [PDF]
English Studies at NBU, 2015 The paper discusses the attitude of Bulgarian linguistic circles towards American generative grammar at its birth and establishment in the period from the 1950s to the 1970s.
Tzvetomira Venkova
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Learning of Structurally Unambiguous Probabilistic Grammars [PDF]
Logical Methods in Computer Science, Volume 19, Issue 1 (February
8, 2023) lmcs:9223, 2022 The problem of identifying a probabilistic context free grammar has two aspects: the first is determining the grammar's topology (the rules of the grammar) and the second is estimating probabilistic weights for each rule. Given the hardness results for learning context-free grammars in general, and probabilistic grammars in particular, most of the ...
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Cadernos de Linguística, 2021 Semantic Syntax (SeSyn), originally called Generative Semantics, is an offshoot of Chomskyan generative grammar (ChoGG), rejected by Chomsky and his school in the late 1960s.
Pieter Seuren
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