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: 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: 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: 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: 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: Jean L. Turner
Publisher: Routledge
ISBN: 1134055587
Category : Education
Languages : en
Pages : 419

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Book Description
Assuming no familiarity with statistical methods, this text for language education research methods and statistics courses provides detailed guidance and instruction on principles of designing, conducting, interpreting, reading, and evaluating statistical research done in classroom settings or with a small number of participants. While three different types of statistics are addressed (descriptive, parametric, non-parametric) the emphasis is on non-parametric statistics because they are appropriate when the number of participants is small and the conditions for use of parametric statistics are not satisfied. The emphasis on non-parametric statistics is unique and complements the growing interest among second and foreign language educators in doing statistical research in classrooms. Designed to help students and other language education researchers to identify and use analyses that are appropriate for their studies, taking into account the number of participants and the shape of the data distribution, the text includes sample studies to illustrate the important points in each chapter and exercises to promote understanding of the concepts and the development of practical research skills. Mathematical operations are explained in detail, and step-by-step illustrations in the use of R (a very powerful, online, freeware program) to perform all calculations are provided. A Companion Website extends and enhances the text with PowerPoint presentations illustrating how to carry out calculations and use R; practice exercises with answer keys; data sets in Excel MS-DOS format; and quiz, midterm, and final problems with answer keys.

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: M. Gareth Gaskell
Publisher: Oxford University Press, USA
ISBN: 0198568975
Category : Language Arts & Disciplines
Languages : en
Pages : 880

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Book Description
The ability to communicate through spoken and written language is one of the defining characteristics of the human race, yet it remains a deeply mysterious process. The young science of psycholinguistics attempts to uncover the mechanisms and representations underlying human language. This interdisciplinary field has seen massive developments over the past decade, with a broad expansion of the research base, and the incorporation of new experimental techniques such as brain imaging and computational modelling. The result is that real progress is being made in the understanding of the key components of language in the mind. The Oxford Handbook of Psycholinguistics brings together the views of 75 leading researchers in psycholinguistics to provide a comprehensive and authoritative review of the current state of the art in psycholinguistics. With almost 50 chapters written by experts in the field, the range and depth of coverage is unequalled. The contributors are eminent in a wide range of fields, including psychology, linguistics, human memory, cognitive neuroscience, bilingualism, genetics, development and neuropsychology. Their contributions are organised into six themed sections, covering word recognition, the mental lexicon, comprehension and discourse, language production, language development, and perspectives on psycholinguistics. The breadth of coverage, coupled with the accessibility of the short chapter format should make the handbook essential reading for both students and researchers in the fields of psychology, linguistics and neuroscience.

Author: Masashi Sugiyama
Publisher: Morgan Kaufmann
ISBN: 0128023503
Category : Mathematics
Languages : en
Pages : 535

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Book Description
Machine learning allows computers to learn and discern patterns without actually being programmed. When Statistical techniques and machine learning are combined together they are a powerful tool for analysing various kinds of data in many computer science/engineering areas including, image processing, speech processing, natural language processing, robot control, as well as in fundamental sciences such as biology, medicine, astronomy, physics, and materials. Introduction to Statistical Machine Learning provides a general introduction to machine learning that covers a wide range of topics concisely and will help you bridge the gap between theory and practice. Part I discusses the fundamental concepts of statistics and probability that are used in describing machine learning algorithms. Part II and Part III explain the two major approaches of machine learning techniques; generative methods and discriminative methods. While Part III provides an in-depth look at advanced topics that play essential roles in making machine learning algorithms more useful in practice. The accompanying MATLAB/Octave programs provide you with the necessary practical skills needed to accomplish a wide range of data analysis tasks. Provides the necessary background material to understand machine learning such as statistics, probability, linear algebra, and calculus Complete coverage of the generative approach to statistical pattern recognition and the discriminative approach to statistical machine learning Includes MATLAB/Octave programs so that readers can test the algorithms numerically and acquire both mathematical and practical skills in a wide range of data analysis tasks Discusses a wide range of applications in machine learning and statistics and provides examples drawn from image processing, speech processing, natural language processing, robot control, as well as biology, medicine, astronomy, physics, and materials

Author: Vladimir Vapnik
Publisher: Springer Science & Business Media
ISBN: 1475732643
Category : Mathematics
Languages : en
Pages : 324

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Book Description
The aim of this book is to discuss the fundamental ideas which lie behind the statistical theory of learning and generalization. It considers learning as a general problem of function estimation based on empirical data. Omitting proofs and technical details, the author concentrates on discussing the main results of learning theory and their connections to fundamental problems in statistics. This second edition contains three new chapters devoted to further development of the learning theory and SVM techniques. Written in a readable and concise style, the book is intended for statisticians, mathematicians, physicists, and computer scientists.