Natural Language Annotation for Machine Learning

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

Become proficient in deriving insights from time-series data and analyzing a model's performanceKey Features: Explore popular and modern machine learning methods including the latest online and deep learning algorithmsLearn to increase the accuracy of your predictions by matching the right model with the right problemMaster time-series via real-world case studies on operations management, digital marketing, finance, and healthcareBook Description: Machine learning has emerged as a powerful tool to understand hidden complexities in time-series datasets, which frequently need to be analyzed in areas as diverse as healthcare, economics, digital marketing, and social sciences. These datasets are essential for forecasting and predicting outcomes or for detecting anomalies to support informed decision making.This book covers Python basics for time-series and builds your understanding of traditional autoregressive models as well as modern non-parametric models. You will become confident with

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.

This modern and self-contained book offers a clear and accessible introduction to the important topic of machine learning with neural networks. In addition to describing the mathematical principles of the topic, and its historical evolution, strong connections are drawn with underlying methods from statistical physics and current applications within science and engineering. Closely based around a well-established undergraduate course, this pedagogical text provides a solid understanding of the key aspects of modern machine learning with artificial neural networks, for students in physics, mathematics, and engineering. Numerous exercises expand and reinforce key concepts within the book and allow students to hone their programming skills. Frequent references to current research develop a detailed perspective on the state-of-the-art in machine learning research.