Statistical Language Learning

This text introduces statistical language processing techniques—word tagging, parsing with probabilistic context free grammars, grammar induction, syntactic disambiguation, semantic word classes, word

This text introduces statistical language processing techniques—word tagging, parsing with probabilistic context free grammars, grammar induction, syntactic disambiguation, semantic word classe

Statistical models recently developed in several research communities (natural language processing, information retrieval, machine learning) for textual information access pertain to several applicati

Information Theory and Statistical Learning presents theoretical and practical results about information theoretic methods used in the context of statistical learning. The book will present a compreh

With a machine learning approach and less focus on linguistic details, this gentle introduction to natural language processing develops fundamental mathematical and deep learning models for NLP under a unified framework. NLP problems are systematically organised by their machine learning nature, including classification, sequence labelling, and sequence-to-sequence problems. Topics covered include statistical machine learning and deep learning models, text classification and structured prediction models, generative and discriminative models, supervised and unsupervised learning with latent variables, neural networks, and transition-based methods. Rich connections are drawn between concepts throughout the book, equipping students with the tools needed to establish a deep understanding of NLP solutions, adapt existing models, and confidently develop innovative models of their own. Featuring a host of examples, intuition, and end of chapter exercises, plus sample code available as an onli

This edited book focuses on the latest developments in classification, statistical learning, data analysis and related areas of data science, including statistical analysis of large datasets, big data

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 a

This book presents a coarse-to-fine framework for learning and inference in large statistical models for natural language processing. The text shows applications of this fast, accurate approach to syn

This book is for anyone who has biomedical data and needs to identify variables that predict an outcome, for two-group outcomes such as tumor/not-tumor, survival/death, or response from treatment. Statistical learning machines are ideally suited to these types of prediction problems, especially if the variables being studied may not meet the assumptions of traditional techniques. Learning machines come from the world of probability and computer science but are not yet widely used in biomedical research. This introduction brings learning machine techniques to the biomedical world in an accessible way, explaining the underlying principles in nontechnical language and using extensive examples and figures. The authors connect these new methods to familiar techniques by showing how to use the learning machine models to generate smaller, more easily interpretable traditional models. Coverage includes single decision trees, multiple-tree techniques such as Random Forests™, neural nets, suppor

This book is for anyone who has biomedical data and needs to identify variables that predict an outcome, for two-group outcomes such as tumor/not-tumor, survival/death, or response from treatment. Statistical learning machines are ideally suited to these types of prediction problems, especially if the variables being studied may not meet the assumptions of traditional techniques. Learning machines come from the world of probability and computer science but are not yet widely used in biomedical research. This introduction brings learning machine techniques to the biomedical world in an accessible way, explaining the underlying principles in nontechnical language and using extensive examples and figures. The authors connect these new methods to familiar techniques by showing how to use the learning machine models to generate smaller, more easily interpretable traditional models. Coverage includes single decision trees, multiple-tree techniques such as Random Forests™, neural nets, suppor

Statistical Learning from a Regression Perspective considers statistical learning applications when interest centers on the conditional distribution of the response variable, given a set of predictors

A comprehensive look at learning and generalization theory. The statistical theory of learning and generalization concerns the problem of choosing desired functions on the basis of empirical data. Hig

This book introduces artificial neural networks to students and professionals. It covers the theory and applications in statistical learning methods with concrete Python code examples.

Advanced statistical modeling and knowledge representation techniques for a newly emerging area of machine learning and probabilistic reasoning; includes introductory material, tutorials for different

This book opens up new lines of debate in language learning and intercultural communication through an investigation of tandem language learning (a method of language learning based on mutual language

This module on technology in language learning provides novice and experienced second and foreign language (L2) teachers alike with an introduction to the field of computer-assisted language learning

Statistical Regression and Classification: From Linear Models to Machine Learning takes an innovative look at the traditional statistical regression course, presenting a contemporary treatment in line

This volume will cover a variety of topics, including child language development; hearing loss; listening in noise; statistical learning; poverty; auditory processing disorder; cochlear neuropathy; at

Providing a broad but in-depth introduction to neural network and machine learning in a statistical framework, this book provides a single, comprehensive resource for study and further research. All t

Second Language Learning and Language Teaching provides an introduction to the application of second language acquisition research to language teaching. Assuming no previous background in second langu

Second Language Learning and Language Teaching provides an introduction to the application of second language acquisition research to language teaching. Assuming no previous background in second langu

This volume focuses on computer- and digitally-assisted language learning in all of its forms: technology-enhanced language learning, network-based language learning, mobile-assisted language learning

The Intersection of Library Learning and Second-Language Learning: Theory and Practice is an examination of how the intersections of language learning and library learning are experienced by language

SALT provides a step-by-step approach to the most useful statistical analyses for language test developers and researchers based on the programs IBM SPSS, Winsteps and Facets. Each chapter focuses on

SALT provides a step-by-step approach to the most useful statistical analyses for language test developers and researchers based on the programs IBM SPSS, Winsteps and Facets. Each chapter focuses on

Strategies in Learning and Using a Second Language examines what it takes to achieve long-term success in languages beyond the first language. Distinguishing language learning from language-use strate

"A joint endeavor from leading researchers in the fields of philosophy and electrical engineering An Introduction to Statistical Learning Theory provides a broad and accessible introduction to rapidly

This textbook provides an overview of theories, methodologies, and recent developments in the field of statistical learning and statistical analysis for visual pattern modeling and computing. The boo

Advanced statistical modeling and knowledge representation techniques for a newlyemerging area of machine learning and probabilistic reasoning; includes introductory material,tutorials for different p

Learning from Data focuses on how to interpret psychological data and statistical results. The authors review the basics of statistical reasoning to helpstudents better understand relevant data that a

In Reliable Reasoning, Gilbert Harman and Sanjeev Kulkarni argue that philosophy and cognitive science can benefit from statistical learning theory (SLT), the theory that lies behind recent advances i

This new DVD to accompany the best-selling sign language book, Learning American Sign Language, brings the art and learning of sign language to life.

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

Language is integral to human doing, being, becoming and belonging, and its acquisition is naturally distributed in and across activity spaces over time. Different learning experiences form 'a dialectical unity', where one brings the others into existence, and the capacities fostered in one inform and transform those in the others. Thus, connected learning across time and space is fundamental to the coherence, relevance, and meaningfulness of language learning, yet is not given sufficient attention in second language education. Connected learning is particularly relevant in the GenAI era where learner-driven lifelong and lifewide learning is much emphasized. This Element hence conceptualizes the framework of connected language learning with technology, and discusses how the framework could be operationalized and implemented in teaching and learning. It further foregrounds four concepts in this learning framework - agential literacy, interest development, self-regulation support, and id

Data-Driven Learning (DDL) can be broadly defined as the use of corpus tools and techniques for learners and teachers of foreign or second language, typically in the form of concordances derived from authentic texts for inductive learning of lexicogrammar. This Element is a practical guide for language teachers and graduate students intending to explore or upgrade their use of corpora in the language classroom and beyond. In today's context, where advances in computing and information processing dominate our social and professional interactions, the use of corpora emerges as a prime resource with which to approach data-driven language learning and teaching, developing language awareness, noticing skills and critical thinking for learning that generative AI cannot do for you.

Due to the rapid development of gaming technologies in recent years, there has been a surge of interest in the role that digital games can play in foreign and second language learning. Bringing together innovative research from an international team of contributors, this book provides a comprehensive overview of the use of digital games in computer-assisted language learning (CALL).The book firstly lays the theoretical foundations and outlines various rationales for using digital games, incorporating contemporary theories of second language acquisition. It also explores the development and impact of digital games designed specifically for language learning, giving due consideration to design principles, pedagogical requirements and student health. Chapters then draw on case studies from Europe and Japan to analyse in-game interaction, attitudes and participation in both institutional and out-of-classroom settings. Seamlessly combining theory with practical application, this book outlin

Data assimilation is a hugely important mathematical technique, relevant in fields as diverse as geophysics, data science, and neuroscience. This modern book provides an authoritative treatment of the field as it relates to several scientific disciplines, with a particular emphasis on recent developments from machine learning and its role in the optimisation of data assimilation. Underlying theory from statistical physics, such as path integrals and Monte Carlo methods, are developed in the text as a basis for data assimilation, and the author then explores examples from current multidisciplinary research such as the modelling of shallow water systems, ocean dynamics, and neuronal dynamics in the avian brain. The theory of data assimilation and machine learning is introduced in an accessible and unified manner, and the book is suitable for undergraduate and graduate students from science and engineering without specialized experience of statistical physics.

Learning language and using language to learn is at the core of any educational activity. Bringing together a globally representative team of experts, this volume presents an innovative and empirically robust collection of studies that examine the role of language in education, with a particular emphasis on features of school-relevant language in middle childhood and adolescents, and its precursors in early childhood. It addresses issues such as how children's linguistic and literacy experiences at home prepare them for school, how the classroom functions as a language-mediated learning environment, and how schools can support language minority students in academic attainment. Set in three parts - Early Childhood, Middle Childhood and Adolescence and Learning in Multilingual Contexts - each part features a discussion from experts in the field to stimulate conversation and further routes for research. Its structure will make it useful for anyone interested in ongoing efforts towards bui

Providing a comprehensive survey of cutting-edge work on second language learning, this Handbook, written by a team of leading experts, surveys the nature of second language learning and its implications for teaching. Prominent theories and methods from linguistics, psycholinguistics, processing-based, and cognitive approaches are covered and organised thematically across sections dealing with skill development, individual differences, pedagogical interventions and approaches, and context and environment. This state-of-the-art volume will interest researchers in second language studies and language education, and will also reach out to advanced undergraduate and graduate students in these and other related areas.