Current language technology is dominated by approaches that either enumerate a large set of rules, or are focused on a large amount of manually labelled data. The creation of both is time-consuming and expensive, which is commonly thought to be the reason why automated natural language understanding has still not made its way into “real-life” applications yet. This book sets an ambitious goal: to shift the development of language processing systems to a much more automated setting than previous works. A new approach is defined: what if computers analysed large samples of language data on their own, identifying structural regularities that perform the necessary abstractions and generalisations in order to better understand language in the process? After defining the framework of Structure Discovery and shedding light on the nature and the graphic structure of natural language data, several procedures are described that do exactly this: let the computer discover structures without supervision in order to boost the performance of language technology applications. Here, multilingual documents are sorted by language, word classes are identified, and semantic ambiguities are discovered and resolved without using a dictionary or other explicit human input. The book concludes with an outlook on the possibilities implied by this paradigm and sets the methods in perspective to human computer interaction. The target audience are academics on all levels (undergraduate and graduate students, lecturers and professors) working in the fields of natural language processing and computational linguistics, as well as natural language engineers who are seeking to improve their systems.
Managing expatriates and other ‘traditional’ internationally mobile workers is a significant part of many academic programmes and the focus of some specialist ones. But we cannot answer the big questions about global mobility if we exclude from our teaching people who do not fit with our usual conceptions and assumptions about who it is that organisations employ.
This new edition of Globalizing Human Resource Management examines the strategic and global issues of HRM by showing how organizations address the tradeoffs between global integration and local responsiveness. Sparrow, Brewster, and Chung discuss varying methods of globalized talent management and employer branding and conclude with a multi-dimensional approach to HRM. The second edition includes: Updated analyses of talent management, employer branding, and outsourcing of HRM Broader geographic focus, including a new focus on Asian firms and other emerging markets Exploration of the impact of strategic management thinking on HR as well as the latest research in other areas, such as operations, marketing, and economic geography Complementing traditional international HRM texts, this is an ideal book for any student interested in the actual strategic logics being pursued by the HR function today.
To a substantial degree cinema has served to define the perceived character of the peoples and nations of the Middle East. This book covers the production and exhibition of the cinema of Morocco, Algeria, Tunisia, Egypt, Palestine, Jordan, Lebanon, Syria, Iraq, the United Arab Emirates, Saudi Arabi, Yemen, Kuwait, and Bahrain, as well as the non-Arab states of Turkey and Iran, and the Jewish state of Israel. This second edition of Historical Dictionary of Middle Eastern Cinema contains a chronology, an introduction, and an extensive bibliography. The dictionary section has over 500 cross-referenced entries on individual films, filmmakers, actors, significant historical figures, events, and concepts, and the countries themselves. It also covers the range of cinematic modes from documentary to fiction, representational to animation, generic to experimental, mainstream to avant-garde, and entertainment to propaganda. This book is an excellent resource for students, researchers, and anyone wanting to know more about Middle Eastern cinema.
The definitive guide to peptidomics- a hands-on lab reference The first truly comprehensive book about peptidomics for protein and peptide analysis, this reference provides a detailed description of the hows and whys of peptidomics and how the techniques have evolved. With chapters contributed by leading experts, it covers naturally occurring peptides, peptidomics methods and new developments, and the peptidomics approach to biomarker discovery. Explaining both the principles and the applications, Peptidomics: Methods and Applications: * Features examples of applications in diverse fields, including pharmaceutical science, toxicity biomarkers, and neuroscience * Details the successful peptidomic analyses of biological material ranging from plants to mammals * Describes a cross section of analytical techniques, including traditional methodologies, emerging trends, and new techniques for high throughput approaches An enlightening reference for experienced professionals, this book is sufficiently detailed to serve as a step-by-step guide for beginning researchers and an excellent resource for students taking biotechnology and proteomics courses. It is an invaluable reference for protein chemists and biochemists, professionals and researchers in drug and biopharmaceutical development, analytical and bioanalytical chemists, toxicologists, and others.
Current language technology is dominated by approaches that either enumerate a large set of rules, or are focused on a large amount of manually labelled data. The creation of both is time-consuming and expensive, which is commonly thought to be the reason why automated natural language understanding has still not made its way into “real-life” applications yet. This book sets an ambitious goal: to shift the development of language processing systems to a much more automated setting than previous works. A new approach is defined: what if computers analysed large samples of language data on their own, identifying structural regularities that perform the necessary abstractions and generalisations in order to better understand language in the process? After defining the framework of Structure Discovery and shedding light on the nature and the graphic structure of natural language data, several procedures are described that do exactly this: let the computer discover structures without supervision in order to boost the performance of language technology applications. Here, multilingual documents are sorted by language, word classes are identified, and semantic ambiguities are discovered and resolved without using a dictionary or other explicit human input. The book concludes with an outlook on the possibilities implied by this paradigm and sets the methods in perspective to human computer interaction. The target audience are academics on all levels (undergraduate and graduate students, lecturers and professors) working in the fields of natural language processing and computational linguistics, as well as natural language engineers who are seeking to improve their systems.
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