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The mechanical method of translating the Bible is a new and unique style of translating that translates each Hebrew word, prefix and suffix exactly the same way every time it occurs and in the same order as they appear in the Hebrew text. This translation will allow a reader, who has no background in Hebrew, to see the text from a Hebraic perspective, without the interjection of a translator's theological opinions and bias. As this style of translation also identifies the morphology of each Hebrew word using the English language, it is a useful tool for those who are learning to read Biblical Hebrew.
Reviews developments in mechanical translation programs. Also considers NSF and CIA programs in comparison with research developments abroad, especially in the Soviet Union.
The field of machine translation (MT) - the automation of translation between human languages - has existed for more than 50 years. MT helped to usher in the field of computational linguistics and has influenced methods and applications in knowledge representation, information theory, and mathematical statistics.
Machine translation (MT) was one of the first non-numerical applications of the computer in the 1950s and 1960s. With limited equipment and programming tools, researchers from a wide range of disciplines (electronics, linguistics, mathematics, engineering, etc.) tackled the unknown problems of language analysis and processing, investigated original and innovative methods and techniques, and laid the foundations not just of current MT systems and computerized tools for translators but also of natural language processing in general. This volume contains contributions by or about the major MT pioneers from the United States, Russia, East and West Europe, and Japan, with recollections of personal experiences, colleagues and rivals, the political and institutional background, the successes and disappointments, and above all the challenges and excitement of a new field with great practical importance. Each article includes a personal bibliography, and the editor provides an overview, chronology and list of sources for the period.
How Machine Learning can improve machine translation: enabling technologies and new statistical techniques.
Machine Translation (MT) has become widely used throughout the world as a medium of communication between those who live in different countries and speak different languages. However, translation between distant languages constitutes a challenge for machines. Therefore, translation evaluation is poised to play a significant role in the process of designing and developing effective MT systems. This book evaluates three prominent MT systems, including Google Translate, Microsoft Translator, and Sakhr, each of which provides translation between English and Arabic. In the book Almahasees scrutinizes the capacity of the three systems in dealing with translation between English and Arabic in a large corpus taken from various domains, including the United Nation (UN), the World Health Organization (WHO), the Arab League, Petra News Agency reports, and two literary texts: The Old Man and the Sea and The Prophet. The evaluation covers holistic analysis to assess the output of the three systems in terms of Translation Automation User Society (TAUS) adequacy and fluency scales. The text also looks at error analysis to evaluate the systems’ output in terms of orthography, lexis, grammar, and semantics at the entire-text level and in terms of lexis, grammar, and semantics at the collocation level. The research findings contained within this volume provide important feedback about the capabilities of the three MT systems with respect to EnglishArabic translation and paves the way for further research on such an important topic. This book will be of interest to scholars and students of translation studies and translation technology.
TRENDS IN LINGUISTICS is a series of books that open new perspectives in our understanding of language. The series publishes state-of-the-art work on core areas of linguistics across theoretical frameworks as well as studies that provide new insights by building bridges to neighbouring fields such as neuroscience and cognitive science. TRENDS IN LINGUISTICS considers itself a forum for cutting-edge research based on solid empirical data on language in its various manifestations, including sign languages. It regards linguistic variation in its synchronic and diachronic dimensions as well as in its social contexts as important sources of insight for a better understanding of the design of linguistic systems and the ecology and evolution of language. TRENDS IN LINGUISTICS publishes monographs and outstanding dissertations as well as edited volumes, which provide the opportunity to address controversial topics from different empirical and theoretical viewpoints. High quality standards are ensured through anonymous reviewing.
Deep learning is revolutionizing how machine translation systems are built today. This book introduces the challenge of machine translation and evaluation - including historical, linguistic, and applied context -- then develops the core deep learning methods used for natural language applications. Code examples in Python give readers a hands-on blueprint for understanding and implementing their own machine translation systems. The book also provides extensive coverage of machine learning tricks, issues involved in handling various forms of data, model enhancements, and current challenges and methods for analysis and visualization. Summaries of the current research in the field make this a state-of-the-art textbook for undergraduate and graduate classes, as well as an essential reference for researchers and developers interested in other applications of neural methods in the broader field of human language processing.