Translation tools and technologies, Rothwell, Andrew Moorkens, Joss Fernandez-parra, Maria Drugan, Joanna Austermuehl, Frank
Автор: Rothwell, Andrew Moorkens, Joss Fernandez-parra, M Название: Translation tools and technologies ISBN: 0367750325 ISBN-13(EAN): 9780367750329 Издательство: Taylor&Francis Рейтинг: Цена: 33670.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Demystifying the workings of Computer-Assisted Translation (CAT) and Machine Translation (MT) technologies, this book offers clear step-by-step guidance on how to choose suitable tools (free or commercial) for the task in hand and quickly get up to speed with them, using examples from a wide range of languages.
Автор: Helena Moniz Название: Towards responsible machine translation ISBN: 3031146883 ISBN-13(EAN): 9783031146886 Издательство: Springer Рейтинг: Цена: 121110.00 T Наличие на складе: Есть у поставщика Поставка под заказ.
Автор: Moorkens Название: Translation Quality Assessment ISBN: 3319912402 ISBN-13(EAN): 9783319912400 Издательство: Springer Рейтинг: Цена: 149060.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This is the first volume that brings together research and practice from academic and industry settings and a combination of human and machine translation evaluation. Its comprehensive collection of papers by leading experts in human and machine translation quality and evaluation who situate current developments and chart future trends fills a clear gap in the literature. This is critical to the successful integration of translation technologies in the industry today, where the lines between human and machine are becoming increasingly blurred by technology: this affects the whole translation landscape, from students and trainers to project managers and professionals, including in-house and freelance translators, as well as, of course, translation scholars and researchers. The editors have broad experience in translation quality evaluation research, including investigations into professional practice with qualitative and quantitative studies, and the contributors are leading experts in their respective fields, providing a unique set of complementary perspectives on human and machine translation quality and evaluation, combining theoretical and applied approaches.
Автор: Anders Sogaard Название: Semi-Supervised Learning and Domain Adaptation in Natural Language Processing ISBN: 1608459853 ISBN-13(EAN): 9781608459858 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 31410.00 T Наличие на складе: Невозможна поставка. Описание: Introduces basic supervised learning algorithms applicable to natural language processing (NLP) and shows how the performance of these algorithms can often be improved by exploiting the marginal distribution of large amounts of unlabelled data. The book introduces extensions of supervised learning algorithms to cope with data sparsity and different kinds of sampling bias.
Автор: Joss Moorkens; Sheila Castilho; Federico Gaspari; Название: Translation Quality Assessment ISBN: 3030082067 ISBN-13(EAN): 9783030082062 Издательство: Springer Рейтинг: Цена: 149060.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This is the first volume that brings together research and practice from academic and industry settings and a combination of human and machine translation evaluation. Its comprehensive collection of papers by leading experts in human and machine translation quality and evaluation who situate current developments and chart future trends fills a clear gap in the literature. This is critical to the successful integration of translation technologies in the industry today, where the lines between human and machine are becoming increasingly blurred by technology: this affects the whole translation landscape, from students and trainers to project managers and professionals, including in-house and freelance translators, as well as, of course, translation scholars and researchers. The editors have broad experience in translation quality evaluation research, including investigations into professional practice with qualitative and quantitative studies, and the contributors are leading experts in their respective fields, providing a unique set of complementary perspectives on human and machine translation quality and evaluation, combining theoretical and applied approaches.
Автор: Goldberg Yoav Название: Neural Network Methods in Natural Language Processing ISBN: 1627052984 ISBN-13(EAN): 9781627052986 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 76690.00 T Наличие на складе: Нет в наличии. Описание: Neural networks are a family of powerful machine learning models. This book focuses on the application of neural network models to natural language data. The first half of the book (Parts I and II) covers the basics of supervised machine learning and feed-forward neural networks, the basics of working with machine learning over language data, and the use of vector-based rather than symbolic representations for words. It also covers the computation-graph abstraction, which allows to easily define and train arbitrary neural networks, and is the basis behind the design of contemporary neural network software libraries.The second part of the book (Parts III and IV) introduces more specialized neural network architectures, including 1D convolutional neural networks, recurrent neural networks, conditioned-generation models, and attention-based models. These architectures and techniques are the driving force behind state-of-the-art algorithms for machine translation, syntactic parsing, and many other applications. Finally, we also discuss tree-shaped networks, structured prediction, and the prospects of multi-task learning.
Автор: Meng Ji, Michael Oakes Название: Advances in Empirical Translation Studies : Developing Translation Resources and Technologies ISBN: 1108423272 ISBN-13(EAN): 9781108423274 Издательство: Cambridge Academ Рейтинг: Цена: 109830.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: An advanced resource for a rapidly evolving research field, aimed at students and academics of translation studies and related fields such as applied linguistics, corpus linguistics, translation technology and digital humanities.
Автор: Horacio Saggion Название: Automatic Text Simplification ISBN: 1627058680 ISBN-13(EAN): 9781627058681 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 51750.00 T Наличие на складе: Невозможна поставка. Описание: Presents research in text simplification, exploring key issues, including automatic readability assessment, lexical simplification, and syntactic simplification. It provides a detailed account of machine learning techniques currently used in simplification, describes full systems designed for specific languages and target audiences, and offers available resources for research and development.
Автор: Inderjeet Mani Название: Computational Modeling of Narrative ISBN: 1608459810 ISBN-13(EAN): 9781608459810 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 41580.00 T Наличие на складе: Невозможна поставка. Описание: Provides an overview of the principal problems, approaches, and challenges faced today in modelling the narrative structure of stories. The book introduces classical narratological concepts from literary theory and their mapping to computational approaches. It also demonstrates how research in AI and NLP has modeled character goals, causality, and time.
Автор: Emily M. Bender Название: Linguistic Fundamentals for Natural Language Processing: 100 Essentials from Morphology and Syntax ISBN: 1627050116 ISBN-13(EAN): 9781627050111 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 46200.00 T Наличие на складе: Невозможна поставка. Описание: Presents in a succinct and accessible fashion information about the morphological and syntactic structure of human languages that can be useful in creating more linguistically sophisticated, more language-independent, and thus more successful NLP systems.
Natural language processing (NLP) went through a profound transformation in the mid-1980s when it shifted to make heavy use of corpora and data-driven techniques to analyze language.
Since then, the use of statistical techniques in NLP has evolved in several ways. One such example of evolution took place in the late 1990s or early 2000s, when full-fledged Bayesian machinery was introduced to NLP. This Bayesian approach to NLP has come to accommodate various shortcomings in the frequentist approach and to enrich it, especially in the unsupervised setting, where statistical learning is done without target prediction examples.
In this book, we cover the methods and algorithms that are needed to fluently read Bayesian learning papers in NLP and to do research in the area. These methods and algorithms are partially borrowed from both machine learning and statistics and are partially developed "in-house" in NLP. We cover inference techniques such as Markov chain Monte Carlo sampling and variational inference, Bayesian estimation, and nonparametric modeling. In response to rapid changes in the field, this second edition of the book includes a new chapter on representation learning and neural networks in the Bayesian context. We also cover fundamental concepts in Bayesian statistics such as prior distributions, conjugacy, and generative modeling. Finally, we review some of the fundamental modeling techniques in NLP, such as grammar modeling, neural networks and representation learning, and their use with Bayesian analysis.
Автор: Lotem Peled-Cohen, Roi Reichart, Rotem Dror, Segev Shlomov Название: Statistical Significance Testing for Natural Language Processing ISBN: 1681737973 ISBN-13(EAN): 9781681737973 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 66530.00 T Наличие на складе: Нет в наличии. Описание: Data-driven experimental analysis has become the main evaluation tool of Natural Language Processing (NLP) algorithms. In fact, in the last decade, it has become rare to see an NLP paper, particularly one that proposes a new algorithm, that does not include extensive experimental analysis, and the number of involved tasks, datasets, domains, and languages is constantly growing. This emphasis on empirical results highlights the role of statistical significance testing in NLP research: If we, as a community, rely on empirical evaluation to validate our hypotheses and reveal the correct language processing mechanisms, we better be sure that our results are not coincidental.
The goal of this book is to discuss the main aspects of statistical significance testing in NLP. Our guiding assumption throughout the book is that the basic question NLP researchers and engineers deal with is whether or not one algorithm can be considered better than another one. This question drives the field forward as it allows the constant progress of developing better technology for language processing challenges. In practice, researchers and engineers would like to draw the right conclusion from a limited set of experiments, and this conclusion should hold for other experiments with datasets they do not have at their disposal or that they cannot perform due to limited time and resources. The book hence discusses the opportunities and challenges in using statistical significance testing in NLP, from the point of view of experimental comparison between two algorithms. We cover topics such as choosing an appropriate significance test for the major NLP tasks, dealing with the unique aspects of significance testing for non-convex deep neural networks, accounting for a large number of comparisons between two NLP algorithms in a statistically valid manner (multiple hypothesis testing), and, finally, the unique challenges yielded by the nature of the data and practices of the field.
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