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Author: Henk Zeevat
Publisher: Springer
ISBN: 3319170643
Category : Language Arts & Disciplines
Languages : en
Pages : 256
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Book Description
The contributions in this volume focus on the Bayesian interpretation of natural languages, which is widely used in areas of artificial intelligence, cognitive science, and computational linguistics. This is the first volume to take up topics in Bayesian Natural Language Interpretation and make proposals based on information theory, probability theory, and related fields. The methodologies offered here extend to the target semantic and pragmatic analyses of computational natural language interpretation. Bayesian approaches to natural language semantics and pragmatics are based on methods from signal processing and the causal Bayesian models pioneered by especially Pearl. In signal processing, the Bayesian method finds the most probable interpretation by finding the one that maximizes the product of the prior probability and the likelihood of the interpretation. It thus stresses the importance of a production model for interpretation as in Grice’s contributions to pragmatics or in interpretation by abduction.
Author: Henk Zeevat
Publisher: Springer
ISBN: 3319170643
Category : Language Arts & Disciplines
Languages : en
Pages : 256
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Book Description
The contributions in this volume focus on the Bayesian interpretation of natural languages, which is widely used in areas of artificial intelligence, cognitive science, and computational linguistics. This is the first volume to take up topics in Bayesian Natural Language Interpretation and make proposals based on information theory, probability theory, and related fields. The methodologies offered here extend to the target semantic and pragmatic analyses of computational natural language interpretation. Bayesian approaches to natural language semantics and pragmatics are based on methods from signal processing and the causal Bayesian models pioneered by especially Pearl. In signal processing, the Bayesian method finds the most probable interpretation by finding the one that maximizes the product of the prior probability and the likelihood of the interpretation. It thus stresses the importance of a production model for interpretation as in Grice’s contributions to pragmatics or in interpretation by abduction.
Author: Shay Cohen
Publisher: Springer Nature
ISBN: 3031021703
Category : Computers
Languages : en
Pages : 311
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Book Description
Natural language processing (NLP) went through a profound transformation in the mid-1980s when it shifted to make heavy use of corpora and data-driven techniques to analyze language. Since then, the use of statistical techniques in NLP has evolved in several ways. One such example of evolution took place in the late 1990s or early 2000s, when full-fledged Bayesian machinery was introduced to NLP. This Bayesian approach to NLP has come to accommodate various shortcomings in the frequentist approach and to enrich it, especially in the unsupervised setting, where statistical learning is done without target prediction examples. In this book, we cover the methods and algorithms that are needed to fluently read Bayesian learning papers in NLP and to do research in the area. These methods and algorithms are partially borrowed from both machine learning and statistics and are partially developed "in-house" in NLP. We cover inference techniques such as Markov chain Monte Carlo sampling and variational inference, Bayesian estimation, and nonparametric modeling. In response to rapid changes in the field, this second edition of the book includes a new chapter on representation learning and neural networks in the Bayesian context. We also cover fundamental concepts in Bayesian statistics such as prior distributions, conjugacy, and generative modeling. Finally, we review some of the fundamental modeling techniques in NLP, such as grammar modeling, neural networks and representation learning, and their use with Bayesian analysis.
Author: Henk Zeevat
Publisher: BRILL
ISBN: 9004252908
Category : Language Arts & Disciplines
Languages : en
Pages : 236
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Book Description
An utterance is normally produced by a speaker in linear time and the hearer normally correctly identifies the speaker intention in linear time and incrementally. This is hard to understand in a standard competence grammar since languages are highly ambiguous and context-free parsing is not linear. Deterministic utterance generation from intention and n-best Bayesian interpretation, based on the production grammar and the prior probabilities that need to be assumed for other perception do much better. The proposed model uses symbolic grammar and derives symbolic semantic representations, but treats interpretation as just another form of perception. Removing interpretation from grammar is not only empirically motivated, but also makes linguistics a much more feasible enterprise. The importance of Henk Zeevat's new monograph cannot be overstated. Its combination of breadth, formal rigor, and originality is unparalleled in work on the form-meaning interface in human language...Zeevat's is the first proposal which provides a computationally feasible integrated treatment of production and comprehension for pragmatics, semantics, syntax, and even phonology. I recommend it to anyone who combines interests in language, logic, and computation with a sense of adventure. David Beaver, University of Texas at Austin
Author: Shay Cohen
Publisher: Morgan & Claypool Publishers
ISBN: 168173527X
Category : Computers
Languages : en
Pages : 345
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Book Description
Natural language processing (NLP) went through a profound transformation in the mid-1980s when it shifted to make heavy use of corpora and data-driven techniques to analyze language. Since then, the use of statistical techniques in NLP has evolved in several ways. One such example of evolution took place in the late 1990s or early 2000s, when full-fledged Bayesian machinery was introduced to NLP. This Bayesian approach to NLP has come to accommodate various shortcomings in the frequentist approach and to enrich it, especially in the unsupervised setting, where statistical learning is done without target prediction examples. In this book, we cover the methods and algorithms that are needed to fluently read Bayesian learning papers in NLP and to do research in the area. These methods and algorithms are partially borrowed from both machine learning and statistics and are partially developed "in-house" in NLP. We cover inference techniques such as Markov chain Monte Carlo sampling and variational inference, Bayesian estimation, and nonparametric modeling. In response to rapid changes in the field, this second edition of the book includes a new chapter on representation learning and neural networks in the Bayesian context. We also cover fundamental concepts in Bayesian statistics such as prior distributions, conjugacy, and generative modeling. Finally, we review some of the fundamental modeling techniques in NLP, such as grammar modeling, neural networks and representation learning, and their use with Bayesian analysis.
Author: Shay Cohen
Publisher: Morgan & Claypool Publishers
ISBN: 1627054219
Category : Computers
Languages : en
Pages : 276
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Book Description
Natural language processing (NLP) went through a profound transformation in the mid-1980s when it shifted to make heavy use of corpora and data-driven techniques to analyze language. Since then, the use of statistical techniques in NLP has evolved in several ways. One such example of evolution took place in the late 1990s or early 2000s, when full-fledged Bayesian machinery was introduced to NLP. This Bayesian approach to NLP has come to accommodate for various shortcomings in the frequentist approach and to enrich it, especially in the unsupervised setting, where statistical learning is done without target prediction examples. We cover the methods and algorithms that are needed to fluently read Bayesian learning papers in NLP and to do research in the area. These methods and algorithms are partially borrowed from both machine learning and statistics and are partially developed "in-house" in NLP. We cover inference techniques such as Markov chain Monte Carlo sampling and variational inference, Bayesian estimation, and nonparametric modeling. We also cover fundamental concepts in Bayesian statistics such as prior distributions, conjugacy, and generative modeling. Finally, we cover some of the fundamental modeling techniques in NLP, such as grammar modeling and their use with Bayesian analysis.
Author: James Clifford
Publisher: Cambridge University Press
ISBN: 9780521602747
Category : Computers
Languages : en
Pages : 216
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Book Description
Connects the semantics of databases to that of natural language, and links them through a common view of the semantics of time.
Author: Reuben Harry Cohn-Gordon
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
Grice (1975) puts forward a view of linguistic meaning in which conversational agents enrich the semantic interpretation of linguistic expressions by recourse to pragmatic reasoning about their interlocutors and world knowledge. As a simple example, on hearing my friend tell me that she read some of War and Peace, I reason that, had she read all of it, she would have said as much, and accordingly that she read only part. It turns out that this perspective is well suited to a probabilistic formalization. In these terms, linguistic meaning is fully characterized by a joint probability distribution P(W; U) between states of the world W and linguistic expressions U. The Gricean perspective described above corresponds to a factoring of this enormously complex distribution into a semantics [[u]](w) : U -> (W -> {0, 1}, world knowledge P(W) and a pair of agents which reason about each other on the assumption that both are cooperative and have access to a commonly known semantics. This third component, of back and forth reasoning between agents, originates in work in game-theory (Franke, 2009; Lewis, 1969) and has been formalized in probabilistic terms by a class of models often collectively referred to as the Rational Speech Acts (RSA) framework (Frank and Goodman, 2012). By allowing for the construction of models which explain in precise terms how Gricean pressures like informativity and relevance interact with a semantics, this framework allows us to take an intuitive theory and explore its predictions beyond the limits of intuition. But it should be more than a theoretical tool. To the extent that its characterization of meaning is correct, it should allow for the construction of computational systems capable of reproducing the dynamics of opendomain natural language. For instance, on the assumption that humans produce language pragmatically, one would expect systems which generate natural language to most faithfully reproduce human behavior when aiming to be not only truthful, but also informative to a hypothetical interlocutor. Likewise, systems which interpret language in a human-like way should perform best when they model language as being generated by an informative speaker. Despite this, standard approaches to many natural language processing (NLP) tasks, like image captioning (Farhadi et al., 2010; Vinyals et al., 2015), translation (Brown et al., 1990; Bahdanau et al., 2014) and metaphor interpretation (Shutova et al., 2013), only incorporate pragmatic reasoning implicitly (in the sense that a supervised model trained on human data may learn to replicate pragmatic behavior). The approach of this dissertation is to take models which capture dynamics of pragmatic language use and apply them to open-domain settings. In this respect, my work builds on research in this vein for referential expression generation (Monroe and Potts, 2015; Andreas and Klein, 2016a), image captioning (Vedantam et al., 2017) and instruction following (Fried et al., 2017), as well as work using neural networks as generative models in Bayesian cognitive architectures (Wu et al., 2015; Liu et al., 2018). The content of the dissertation divides into two parts. The first (chapter 2) focuses on the interpretation of language (particularly non-literal language) using a model of non-literal language previously applied to hyperbole and metaphor interpretation in a setting with a hand-specified and idealized semantics. Here, the goal is to instantiate the same model, but with a semantics derived from a vector space model of word meaning. In this setting, the model remains unchanged, but states are points in an abstract word embedding space - a central computational linguistic representation of meaning (Mikolov et al., 2013; Pennington et al., 2014). The core idea here is that points in the space can be viewed as a continuous analogue of possible worlds, and that linear projections of a vector space are a natural way to represent the aspect of the world that is relevant in a conversation. The second part of the dissertation (chapters 3 and 4) focuses on the production of language, in settings where the length of utterances (and consequently the set of all possible utterances) is unbounded. The core idea here is that pragmatic reasoning can take place incrementally, that is, midway through the saying or hearing of an utterance. This incremental approach is applied to neural language generation tasks, producing informative image captions and translations. The result of these investigations is far from a complete picture, but nevertheless a substantial step towards Bayesian models of semantics and pragmatics which can handle the full richness of natural language, and by doing so provide both explanatory models of meaning and computational systems for producing and interpreting language.
Author: Ryan M. Nefdt
Publisher: Cambridge University Press
ISBN: 1009085301
Category : Language Arts & Disciplines
Languages : en
Pages : 245
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Book Description
What is the remit of theoretical linguistics? How are human languages different from animal calls or artificial languages? What philosophical insights about language can be gleaned from phonology, pragmatics, probabilistic linguistics, and deep learning? This book addresses the current philosophical issues at the heart of theoretical linguistics, which are widely debated not only by linguists, but also philosophers, psychologists, and computer scientists. It delves into hitherto uncharted territory, putting philosophy in direct conversation with phonology, sign language studies, supersemantics, computational linguistics, and language evolution. A range of theoretical positions are covered, from optimality theory and autosegmental phonology to generative syntax, dynamic semantics, and natural language processing with deep learning techniques. By both unwinding the complexities of natural language and delving into the nature of the science that studies it, this book ultimately improves our tools of discovery aimed at one of the most essential features of our humanity, our language.
Author: Shinji Watanabe
Publisher: Cambridge University Press
ISBN: 1107055571
Category : Computers
Languages : en
Pages : 447
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Book Description
A practical and comprehensive guide on how to apply Bayesian machine learning techniques to solve speech and language processing problems.
Author: Matthew W. Crocker
Publisher: Frontiers Media SA
ISBN: 2889747654
Category : Science
Languages : en
Pages : 514
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Book Description
