Application of Graph Rewriting to Natural Language Processing PDF Download
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Author: Guillaume Bonfante
Publisher: John Wiley & Sons
ISBN: 111952234X
Category : Language Arts & Disciplines
Languages : en
Pages : 213
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Book Description
The paradigm of Graph Rewriting is used very little in the field of Natural Language Processing. But graphs are a natural way of representing the deep syntax and the semantics of natural languages. Deep syntax is an abstraction of syntactic dependencies towards semantics in the form of graphs and there is a compact way of representing the semantics in an underspecified logical framework also with graphs. Then, Graph Rewriting reconciles efficiency with linguistic readability for producing representations at some linguistic level by transformation of a neighbor level: from raw text to surface syntax, from surface syntax to deep syntax, from deep syntax to underspecified logical semantics and conversely.
Author: Guillaume Bonfante
Publisher: John Wiley & Sons
ISBN: 111952234X
Category : Language Arts & Disciplines
Languages : en
Pages : 213
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Book Description
The paradigm of Graph Rewriting is used very little in the field of Natural Language Processing. But graphs are a natural way of representing the deep syntax and the semantics of natural languages. Deep syntax is an abstraction of syntactic dependencies towards semantics in the form of graphs and there is a compact way of representing the semantics in an underspecified logical framework also with graphs. Then, Graph Rewriting reconciles efficiency with linguistic readability for producing representations at some linguistic level by transformation of a neighbor level: from raw text to surface syntax, from surface syntax to deep syntax, from deep syntax to underspecified logical semantics and conversely.
Author: Muskan Garg
Publisher: CRC Press
ISBN: 1000789306
Category : Business & Economics
Languages : en
Pages : 272
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Book Description
Advances in graph-based natural language processing (NLP) and information retrieval tasks have shown the importance of processing using the Graph of Words method. This book covers recent concrete information, from the basics to advanced level, about graph-based learning, such as neural network-based approaches, computational intelligence for learning parameters and feature reduction, and network science for graph-based NPL. It also contains information about language generation based on graphical theories and language models. Features: Presents a comprehensive study of the interdisciplinary graphical approach to NLP Covers recent computational intelligence techniques for graph-based neural network models Discusses advances in random walk-based techniques, semantic webs, and lexical networks Explores recent research into NLP for graph-based streaming data Reviews advances in knowledge graph embedding and ontologies for NLP approaches This book is aimed at researchers and graduate students in computer science, natural language processing, and deep and machine learning.
Author: Janice Cuny
Publisher: Springer Science & Business Media
ISBN: 9783540612285
Category : Computers
Languages : en
Pages : 582
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Book Description
This book describes the functional properties and the structural organization of the members of the thrombospondin gene family. These proteins comprise a family of extracellular calcium binding proteins that modulate cellular adhesion, migration and proliferation. Thrombospondin-1 has been shown to function during angiogenesis, wound healing and tumor cell metastasis.
Author: Thomas, J. Joshua
Publisher: IGI Global
ISBN: 1799811948
Category : Computers
Languages : en
Pages : 355
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Book Description
Many approaches have sprouted from artificial intelligence (AI) and produced major breakthroughs in the computer science and engineering industries. Deep learning is a method that is transforming the world of data and analytics. Optimization of this new approach is still unclear, however, and there’s a need for research on the various applications and techniques of deep learning in the field of computing. Deep Learning Techniques and Optimization Strategies in Big Data Analytics is a collection of innovative research on the methods and applications of deep learning strategies in the fields of computer science and information systems. While highlighting topics including data integration, computational modeling, and scheduling systems, this book is ideally designed for engineers, IT specialists, data analysts, data scientists, engineers, researchers, academicians, and students seeking current research on deep learning methods and its application in the digital industry.
Author: Claire Martinot
Publisher: John Wiley & Sons
ISBN: 111959720X
Category : Language Arts & Disciplines
Languages : en
Pages : 202
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Book Description
Reformulation and Acquisition of Linguistic Complexity proposes a new answer to the question of the appropriation or acquisition of a mother tongue – a complex object, one that is both stable and perpetually evolving. This answer is based on the reformulating principle that children spontaneously apply; a principle that is illustrated here with children retelling the same story. These children are all 6, 8 or 10 years old and speak French, Italian, Croatian or Polish as a first language. This book demonstrates that the acquisition of any mother tongue is explained by the application of various reformulation procedures between source predications and reformulated predications. These procedures are comparable from one language to another, and different from one age group to another. This book also studies certain complex phenomena at the lexical and syntactical levels, and analyzes how children, depending on their age, treat these phenomena. Finally, we show that the acquisition of a mother tongue is a fundamentally linguistic activity.
Author: Christelle Gillioz
Publisher: John Wiley & Sons
ISBN: 178630418X
Category : Language Arts & Disciplines
Languages : en
Pages : 274
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Book Description
The use of experimental methodology in the field of linguistics has boomed in recent decades. However, implementation of such methods does require an understanding and mastery of specific theoretical and methodological principles. Introduction to Experimental Linguistics presents the key concepts of experimental linguistics in an accessible way, addressing, in turn: the application of experimentation in linguistics; the techniques most frequently used for the study of language; the methodological and practical aspects useful for the implementation of an experiment; and an introduction to the analysis of quantitative data derived from experiments. This didactic book combines the elements presented with examples drawn from the various fields of linguistics. It also includes a number of resources available for people who wish to implement an experimental study, more advanced reading suggestions, and revision questions along with their answer key.
Author: François Osiurak
Publisher: John Wiley & Sons
ISBN: 111972032X
Category : Psychology
Languages : en
Pages : 238
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Book Description
Humans use countless tools and are constantly creating new ones. We are so prone to materiality that the changes we implement in our environment could put our very survival at stake. It has therefore become necessary to question the cognitive origins of this materiality. The Tool Instinct examines this subject by diametrically setting aside the idea that tool use is limited to manual activity. It proposes an original perspective that surpasses a great number of false beliefs regarding the relationship between humans and tools. The author argues that the human tendency to create and use tools relies on our ability (one that may be unique to our species) to generate our own physical problems, thereby resulting in a reasoning that is directed towards our physical world.
Author: Shay Cohen
Publisher: Springer Nature
ISBN: 3031021614
Category : Computers
Languages : en
Pages : 266
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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: Joe Tekli
Publisher: Springer Nature
ISBN: 3031704215
Category :
Languages : en
Pages : 413
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Book Description
Author: Annie Foret
Publisher: Springer
ISBN: 3662530422
Category : Mathematics
Languages : en
Pages : 331
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Book Description
This book constitutes the refereed proceedings of the 20th and 21st International Conference on Formal Grammar 2015 and 2016, collocated with the European Summer School in Logic, Language and Information in August 2015/2016. The 19 revised full papers presented together with 2 invited talks were carefully reviewed and selected from a total of 34 submissions. The focus of papers are as follows: Formal and computational phonology, morphology, syntax, semantics and pragmatics Model-theoretic and proof-theoretic methods in linguistics Logical aspects of linguistic structure Constraint-based and resource-sensitive approaches to grammar Learnability of formal grammar Integration of stochastic and symbolic models of grammar Foundational, methodological and architectural issues in grammar and linguistics Mathematical foundations of statistical approaches to linguistic analysis
