...This chapter explores the use of generative models for semi-supervised learning with labeled and unlabeled data in domains of text classification. The widely used naive Bayes classifier for supervised learning defines a mixture of multinomials mixture models. In some domains, model likelihood...

...This chapter examines the theory of norms and exploitations in the context of other theories of language. It considers the role of the lexicon in linguistic theory in twentieth-century Europe since Wilhelm von Humboldt and Ferdinand de Saussure, and also discusses generative linguistics, cognitive...

... as scaffolded phenomena. These three perspectives focus on reproduction, human group coordination, and generative entrenchment in biology and culture. The contributors to the volume span multiple levels and dimensions of analysis. Their contributions scaffolded the goals of the project, sometimes in disparate...

... of the emergence of interchangeable and standardized machine parts that was a major factor in midwifing the industrial revolution. Adaptive radiation Artifacts Combinatorial alphabet Combinatorial entrenchment Combinatorial generative systems Construction alphabet Cultural change evolution Culture Culture...

...The reconstruction of the grammar of word accentuation in Proto-Indo-European (PIE) has for years been a central topic in historical linguistics, as well as the focus of a number of studies within generative phonology of the daughter languages that preserve relicts of the anterior system...

... transition kernel approximating PCFG initialization type token binning auxiliary variable slice sampling span wise conditioning hyperparameters Probabilistic Generative model Chinese restaurant process Dirichlet process Dirichlet distribution Context-free grammar Fragment Grammars Adaptor...

... to enrich the “biolinguistic perspective” and extend it beyond an abstract framework for inquiry. As a theoretical linguist in the generative tradition and also a cognitive scientist schooled in the new imaging technology, the author is equipped to explore this. He examines what he calls the “hidden...

...This chapter consists of four parts. The first section discusses the ideas behind the modeling framework adopted in the book---structured probabilistic generative models. The second section discusses some theoretical and methodological issues in the interpretation of this approach to modeling...

... observer, most crucially including the weakness of backward relative to forward blocking. Other departures from the ideal observer may be due to biological organisms taking into account factors other than belief accuracy. Finally, generative and discriminative learning models are compared. Generative...

...The transformational rules of generative grammar significantly overgenerate the output of natural language, producing grammars that children would find impossible to learn. To address this problem, the generative grammarians of the 1970s and 1980s augmented grammars with output filters such as Noam...

...In his foundational book published in 1995, the author offered a significant contribution to the generative tradition in linguistics. This twentieth-anniversary edition reissues this classic work with a new preface by the author. The author attempts to situate linguistic theory in the broader...

...This chapter discusses the kinds of work being pursued within the general principles-and-parameters model as well as some of the thinking that underlies and guides it. The theory of principles and parameters is a particular approach to classical problems of the study of language, guided by certain...

... and argues that it relies on several distinct learning mechanisms, including error-driven learning at two distinct levels and building a generative model of the speaker. The emergence of perceptual equivalence regions from error-driven learning is discussed, and implications for language change briefly noted...

... an approach that employs hidden Markov random fields (HMRFs) as a probabilistic generative model for semi-supervised clustering, thereby providing a principled framework for incorporating constraint-based supervision into prototype-based clustering. The HMRF-based model allows the use of a broad range...

... a generative model. It shows that the generative model’s trial-to-trial change is a forgetting rate. Presumably, biological learning is a process of state estimation, and a process in which the brain learns the structure of the generative model. Banks Marty Burge Johannes Ernst Marc Rehabilitation Kording...

... psychologically molded cultural attractors as explaining cultural stability. This and the work on conceptual change in ontogeny studied by Carey can generate trans-generational invariance through generative entrenchment, and cumulative developmental change without as strong a need for high fidelity...

... allows the researcher to stay alert and active in the midst of ambiguities and discontinuities that characterize practice settings unsettled by technological innovation and the very practice of doing research. Fleeting moments of disruption can be transformed in generative events and opportunities...

... Ladislao Pablo Haddad Chafic Kosuth Joseph Learning cliff McLuhan Marshall Per severe Per ish Psyop Weiner Lawrence Raley Rita Rumi Bateson Gregory Beddard Tom Easy Font 2011 Generative Knuth Donald Topological letterforms Global Visuage A 2012 Harman Graham Piringer Jörg Clay Craft...

...Ellipsis phenomena in natural language involve instances of meaning without sound as they do, and their interpretation requires the presence of some covert structure. The central issue for a theory of ellipsis is to determine how this structure is constructed. Within generative grammar, two...