Statistical Language Learning PDF Download
Are you looking for read ebook online? Search for your book and save it on your Kindle device, PC, phones or tablets. Download Statistical Language Learning PDF full book. Access full book title Statistical Language Learning by Eugene Charniak. Download full books in PDF and EPUB format.
Author: Eugene Charniak
Publisher: MIT Press
ISBN: 9780262531412
Category : Computers
Languages : en
Pages : 196
Get Book Here
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: Eugene Charniak
Publisher: MIT Press
ISBN: 9780262531412
Category : Computers
Languages : en
Pages : 196
Get Book Here
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
Get Book Here
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: Christopher Manning
Publisher: MIT Press
ISBN: 0262303795
Category : Language Arts & Disciplines
Languages : en
Pages : 719
Get Book Here
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: Carlos Martín-Vide
Publisher: Springer Nature
ISBN: 3030313727
Category : Computers
Languages : en
Pages : 326
Get Book Here
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: Taylor Arnold
Publisher: CRC Press
ISBN: 1351694766
Category : Business & Economics
Languages : en
Pages : 377
Get Book Here
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.
Author: Maggie Tallerman
Publisher: Oxford University Press
ISBN: 0199541116
Category : Language Arts & Disciplines
Languages : en
Pages : 790
Get Book Here
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: Nicholas Allott
Publisher: John Wiley & Sons
ISBN: 1119598702
Category : Philosophy
Languages : en
Pages : 644
Get Book Here
Book Description
A COMPANION TO CHOMSKY Widely considered to be one of the most important public intellectuals of our time, Noam Chomsky has revolutionized modern linguistics. His thought has had a profound impact upon the philosophy of language, mind, and science, as well as the interdisciplinary field of cognitive science which his work helped to establish. Now, in this new Companion dedicated to his substantial body of work and the range of its influence, an international assembly of prominent linguists, philosophers, and cognitive scientists reflect upon the interdisciplinary reach of Chomsky's intellectual contributions. Balancing theoretical rigor with accessibility to the non-specialist, the Companion is organized into eight sections—including the historical development of Chomsky's theories and the current state of the art, comparison with rival usage-based approaches, and the relation of his generative approach to work on linguistic processing, acquisition, semantics, pragmatics, and philosophy of language. Later chapters address Chomsky's rationalist critique of behaviorism and related empiricist approaches to psychology, as well as his insistence upon a "Galilean" methodology in cognitive science. Following a brief discussion of the relation of his work in linguistics to his work on political issues, the book concludes with an essay written by Chomsky himself, reflecting on the history and character of his work in his own words. A significant contribution to the study of Chomsky's thought, A Companion to Chomsky is an indispensable resource for philosophers, linguists, psychologists, advanced undergraduate and graduate students, and general readers with interest in Noam Chomsky's intellectual legacy as one of the great thinkers of the twentieth century.
Author: Masashi Sugiyama
Publisher: Morgan Kaufmann
ISBN: 0128023503
Category : Mathematics
Languages : en
Pages : 535
Get Book Here
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: Frank Emmert-Streib
Publisher: Springer Science & Business Media
ISBN: 0387848150
Category : Computers
Languages : en
Pages : 443
Get Book Here
Book Description
This interdisciplinary text offers theoretical and practical results of information theoretic methods used in statistical learning. It presents a comprehensive overview of the many different methods that have been developed in numerous contexts.
Author: RODRIGO F MELLO
Publisher: Springer
ISBN: 3319949896
Category : Computers
Languages : en
Pages : 373
Get Book Here
Book Description
This book presents the Statistical Learning Theory in a detailed and easy to understand way, by using practical examples, algorithms and source codes. It can be used as a textbook in graduation or undergraduation courses, for self-learners, or as reference with respect to the main theoretical concepts of Machine Learning. Fundamental concepts of Linear Algebra and Optimization applied to Machine Learning are provided, as well as source codes in R, making the book as self-contained as possible. It starts with an introduction to Machine Learning concepts and algorithms such as the Perceptron, Multilayer Perceptron and the Distance-Weighted Nearest Neighbors with examples, in order to provide the necessary foundation so the reader is able to understand the Bias-Variance Dilemma, which is the central point of the Statistical Learning Theory. Afterwards, we introduce all assumptions and formalize the Statistical Learning Theory, allowing the practical study of different classification algorithms. Then, we proceed with concentration inequalities until arriving to the Generalization and the Large-Margin bounds, providing the main motivations for the Support Vector Machines. From that, we introduce all necessary optimization concepts related to the implementation of Support Vector Machines. To provide a next stage of development, the book finishes with a discussion on SVM kernels as a way and motivation to study data spaces and improve classification results.
