Natural Language Annotation for Machine Learning

In the age of data-driven problem-solving, applying sophisticated computational tools for explaining substantive phenomena is a valuable skill. Yet, application of methods assumes an understanding of the data, structure, and patterns that influence the broader research program. This Element offers researchers and teachers an introduction to clustering, which is a prominent class of unsupervised machine learning for exploring and understanding latent, non-random structure in data. A suite of widely used clustering techniques is covered in this Element, in addition to R code and real data to facilitate interaction with the concepts. Upon setting the stage for clustering, the following algorithms are detailed: agglomerative hierarchical clustering, k-means clustering, Gaussian mixture models, and at a higher-level, fuzzy C-means clustering, DBSCAN, and partitioning around medoids (k-medoids) clustering.

This book focuses on the implementation of various elementary and advanced approaches in AI that can be used in various domains to solve real-time decision-making problems.

Bringing together an international team of researchers, this volume explores practice in second language learning - activities which aim to develop skills in or knowledge of a second language. The book begins with two theoretical overviews of practice as applied to learning to speak in a second language and in cognitive accounts of second language acquisition. This theory underpins the volume, which is split into two main sections: receptive and productive practice. The studies look at practice in English, German and Spanish as a second language in various contexts including traditional classrooms, periods of study abroad and online language learning. The differing research designs used mean that the chapters contain clear implications for classroom pedagogy and further directions for research, teaching and learning in different contexts.

With its intuitive yet rigorous approach to machine learning, this text provides students with the fundamental knowledge and practical tools needed to conduct research and build data-driven products. The authors prioritize geometric intuition and algorithmic thinking, and include detail on all the essential mathematical prerequisites, to offer a fresh and accessible way to learn. Practical applications are emphasized, with examples from disciplines including computer vision, natural language processing, economics, neuroscience, recommender systems, physics, and biology. Over 300 color illustrations are included and have been meticulously designed to enable an intuitive grasp of technical concepts, and over 100 in-depth coding exercises (in Python) provide a real understanding of crucial machine learning algorithms. A suite of online resources including sample code, data sets, interactive lecture slides, and a solutions manual are provided online, making this an ideal text both for grad

In applied linguistics, being explicit about ontologies of English, and how they underpin educational ideologies and professional practices, is essential. For the first time, this volume presents a critical examination of the ways in which English is conceptualised for learning, teaching, and assessment, from both social and cognitive perspectives. Written by a team of leading scholars, it considers the language in a range of contexts and domains, including: models and targets for EFL, ESL and EAL teaching and testing, and the contested dominance of native-speaker 'standard' varieties; English as a school subject, using England's educational system as an example; English as a lingua franca, where typically several languages and cultures are in contact; and English as broader social practice in a world characterised by unprecedented mobility and destabilisation. Readers are provided with a balanced set of perspectives on ontologies of English and a valuable resource for educational rese

The use of literature in second language teaching has been advocated for a number of years, yet despite this there have only been a limited number of studies which have sought to investigate its effects. Fewer still have focused on its potential effects as a model of spoken language or as a vehicle to develop speaking skills. Drawing upon multiple research studies, this volume fills that gap to explore how literature is used to develop speaking skills in second language learners. The volume is divided into two sections: literature and spoken language and literature and speaking skills. The first section focuses on studies exploring the use of literature to raise awareness of spoken language features, whilst the second investigates its potential as a vehicle to develop speaking skills. Each section contains studies with different designs and in various contexts including China, Japan and the UK. The research designs used mean that the chapters contain clear implications for classroom pe

This book provides a perspective on the application of machine learning-based methods in knowledge discovery from natural languages texts. By analysing various data sets, conclusions which are not nor

Machine Learning with Spark and Python Essential Techniques for Predictive Analytics Second Edition simplifies ML for practical uses by focusing on two key algorithms. This new second edition improves

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.

Self-driving cars, natural language recognition, and online recommendation engines are all possible thanks to Machine Learning. Now you can create your own genetic algorithms, nature-inspired swarms, Monte Carlo simulations, cellular automata, and clusters. Learn how to test your ML code and dive into even more advanced topics. If you are a beginner-to-intermediate programmer keen to understand machine learning, this book is for you.Discover machine learning algorithms using a handful of self-contained recipes. Build a repertoire of algorithms, discovering terms and approaches that apply generally. Bake intelligence into your algorithms, guiding them to discover good solutions to problems.In this book, you will:Use heuristics and design fitness functions.Build genetic algorithms.Make nature-inspired swarms with ants, bees and particles.Create Monte Carlo simulations.Investigate cellular automata.Find minima and maxima, using hill climbing and simulated annealing.Try selection methods,

Strengthen mathematical skills and gain practice using those skills in preparation for success in machine trades or manufacturing with Peterson/Smith's MATHEMATICS FOR MACHINE TECHNOLOGY, 8E. This com

Using case studies, reflection questions, and research on course design, this book addresses the world language instructor and the diverse learner. Devoted to strategies based on Universal Design for

Using case studies, reflection questions, and research on course design, this book addresses the world language instructor and the diverse learner. Devoted to strategies based on Universal Design for