Author : Eugene Charniak
Publisher : MIT Press
Page : 196 pages
File Size : 36,89 MB
Release : 1996
Category : Computers
ISBN : 9780262531412
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
Page : 617 pages
File Size : 17,89 MB
Release : 2023-08-01
Category : Mathematics
ISBN : 3031387473
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
Page : 524 pages
File Size : 15,97 MB
Release : 2012-10-01
Category : Psychology
ISBN : 1934078247
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
Page : 340 pages
File Size : 19,66 MB
Release : 1986-08-14
Category : Language Arts & Disciplines
ISBN : 9780521273121
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 : Jeffrey Lidz
Publisher : Oxford University Press
Page : 1041 pages
File Size : 48,87 MB
Release : 2016
Category : Computers
ISBN : 0199601267
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 : Christopher Manning
Publisher : MIT Press
Page : 719 pages
File Size : 20,50 MB
Release : 1999-05-28
Category : Language Arts & Disciplines
ISBN : 0262303795
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 : Jenifer Larson-Hall
Publisher : Routledge
Page : 649 pages
File Size : 32,4 MB
Release : 2009-09-10
Category : Education
ISBN : 1135594732
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 : Carlos Martín-Vide
Publisher : Springer Nature
Page : 326 pages
File Size : 16,67 MB
Release : 2019-09-27
Category : Computers
ISBN : 3030313727
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 : Maggie Tallerman
Publisher : Oxford University Press
Page : 790 pages
File Size : 23,69 MB
Release : 2012
Category : Language Arts & Disciplines
ISBN : 0199541116
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 : Masashi Sugiyama
Publisher : Morgan Kaufmann
Page : 535 pages
File Size : 13,46 MB
Release : 2015-10-31
Category : Mathematics
ISBN : 0128023503
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
