Statistical Language Learning PDF Download

Author: Eugene Charniak
Publisher: MIT Press
ISBN: 9780262531412
Category : Computers
Languages : en
Pages : 196

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Book Description
This text introduces statistical language processing techniques--word tagging, parsing with probabilistic context free grammars, grammar induction, syntactic disambiguation, semantic word classes, word-sense disambiguation--along with the underlying mathematics and chapter exercises.

Author: Anthony Woods
Publisher: Cambridge University Press
ISBN: 9780521273121
Category : Language Arts & Disciplines
Languages : en
Pages : 340

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Book Description
Presents a wide variety of linguistic examples to demonstrate the use of statistics in summarizing data appropriately. The range of techniques introduced will help readers to evaluate and use literature employing statistical analysis, and to apply statistics in their own research.

Author: Patrick Rebuschat
Publisher: Walter de Gruyter
ISBN: 1934078247
Category : Psychology
Languages : en
Pages : 524

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Book Description
Open publication This volume brings together contributors from cognitive psychology, theoretical and applied linguistics, as well as computer science, in order to assess the progress made in statistical learning research and to determine future directions. An important objective is to critically examine the role of statistical learning in language acquisition. While most contributors agree that statistical learning plays a central role in language acquisition, they have differing views. This book will promote the development of the field by fostering discussion and collaborations across disciplinary boundaries.

Author: Gareth James
Publisher: Springer Nature
ISBN: 3031387473
Category : Mathematics
Languages : en
Pages : 617

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Book Description
An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance, marketing, and astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, deep learning, survival analysis, multiple testing, and more. Color graphics and real-world examples are used to illustrate the methods presented. This book is targeted at statisticians and non-statisticians alike, who wish to use cutting-edge statistical learning techniques to analyze their data. Four of the authors co-wrote An Introduction to Statistical Learning, With Applications in R (ISLR), which has become a mainstay of undergraduate and graduate classrooms worldwide, as well as an important reference book for data scientists. One of the keys to its success was that each chapter contains a tutorial on implementing the analyses and methods presented in the R scientific computing environment. However, in recent years Python has become a popular language for data science, and there has been increasing demand for a Python-based alternative to ISLR. Hence, this book (ISLP) covers the same materials as ISLR but with labs implemented in Python. These labs will be useful both for Python novices, as well as experienced users.

Author: Jenifer Larson-Hall
Publisher: Routledge
ISBN: 1135594732
Category : Education
Languages : en
Pages : 649

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Book Description
This valuable book shows second language researchers how to use the statistical program SPSS to conduct statistical tests frequently done in SLA research. Using data sets from real SLA studies, A Guide to Doing Statistics in Second Language Research Using SPSS shows newcomers to both statistics and SPSS how to generate descriptive statistics, how to choose a statistical test, and how to conduct and interpret a variety of basic statistical tests. It covers the statistical tests that are most commonly used in second language research, including chi-square, t-tests, correlation, multiple regression, ANOVA and non-parametric analogs to these tests. The text is abundantly illustrated with graphs and tables depicting actual data sets, and exercises throughout the book help readers understand concepts (such as the difference between independent and dependent variables) and work out statistical analyses. Answers to all exercises are provided on the book’s companion website, along with sample data sets and other supplementary material.

Author: Jeffrey Lidz
Publisher: Oxford University Press
ISBN: 0199601267
Category : Computers
Languages : en
Pages : 1041

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Book Description
In this handbook, renowned scholars from a range of backgrounds provide a state of the art review of key developmental findings in language acquisition. The book places language acquisition phenomena in a richly linguistic and comparative context, highlighting the link between linguistic theory, language development, and theories of learning. The book is divided into six parts. Parts I and II examine the acquisition of phonology and morphology respectively, with chapters covering topics such as phonotactics and syllable structure, prosodic phenomena, compound word formation, and processing continuous speech. Part III moves on to the acquisition of syntax, including argument structure, questions, mood alternations, and possessives. In Part IV, chapters consider semantic aspects of language acquisition, including the expression of genericity, quantification, and scalar implicature. Finally, Parts V and VI look at theories of learning and aspects of atypical language development respectively.

Author: Carlos Martín-Vide
Publisher: Springer Nature
ISBN: 3030313727
Category : Computers
Languages : en
Pages : 326

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Book Description
This book constitutes the proceedings of the 7th International Conference on Statistical Language and Speech Processing, SLSP 2019, held in Ljubljana, Slovenia, in October 2019. The 25 full papers presented together with one invited paper in this volume were carefully reviewed and selected from 48 submissions. They were organized in topical sections named: Dialogue and Spoken Language Understanding; Language Analysis and Generation; Speech Analysis and Synthesis; Speech Recognition; Text Analysis and Classification.

Author: Christopher Manning
Publisher: MIT Press
ISBN: 0262303795
Category : Language Arts & Disciplines
Languages : en
Pages : 719

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Book Description
Statistical approaches to processing natural language text have become dominant in recent years. This foundational text is the first comprehensive introduction to statistical natural language processing (NLP) to appear. The book contains all the theory and algorithms needed for building NLP tools. It provides broad but rigorous coverage of mathematical and linguistic foundations, as well as detailed discussion of statistical methods, allowing students and researchers to construct their own implementations. The book covers collocation finding, word sense disambiguation, probabilistic parsing, information retrieval, and other applications.

Author: Maggie Tallerman
Publisher: Oxford University Press
ISBN: 0199541116
Category : Language Arts & Disciplines
Languages : en
Pages : 790

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Book Description
Leading scholars present critical accounts of every aspect of the field, including work in animal behaviour; anatomy, genetics and neurology; the prehistory of language; the development of our uniquely linguistic species; and language creation, transmission, and change.

Author: Taylor Arnold
Publisher: CRC Press
ISBN: 1351694766
Category : Business & Economics
Languages : en
Pages : 377

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Book Description
A Computational Approach to Statistical Learning gives a novel introduction to predictive modeling by focusing on the algorithmic and numeric motivations behind popular statistical methods. The text contains annotated code to over 80 original reference functions. These functions provide minimal working implementations of common statistical learning algorithms. Every chapter concludes with a fully worked out application that illustrates predictive modeling tasks using a real-world dataset. The text begins with a detailed analysis of linear models and ordinary least squares. Subsequent chapters explore extensions such as ridge regression, generalized linear models, and additive models. The second half focuses on the use of general-purpose algorithms for convex optimization and their application to tasks in statistical learning. Models covered include the elastic net, dense neural networks, convolutional neural networks (CNNs), and spectral clustering. A unifying theme throughout the text is the use of optimization theory in the description of predictive models, with a particular focus on the singular value decomposition (SVD). Through this theme, the computational approach motivates and clarifies the relationships between various predictive models. Taylor Arnold is an assistant professor of statistics at the University of Richmond. His work at the intersection of computer vision, natural language processing, and digital humanities has been supported by multiple grants from the National Endowment for the Humanities (NEH) and the American Council of Learned Societies (ACLS). His first book, Humanities Data in R, was published in 2015. Michael Kane is an assistant professor of biostatistics at Yale University. He is the recipient of grants from the National Institutes of Health (NIH), DARPA, and the Bill and Melinda Gates Foundation. His R package bigmemory won the Chamber's prize for statistical software in 2010. Bryan Lewis is an applied mathematician and author of many popular R packages, including irlba, doRedis, and threejs.