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Author: Christian Gaubert
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Languages : un
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Author: Christian Gaubert
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Author: Nahed Hamza Al Imam
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Category :
Languages : ar
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L'Abjad en tant que système d'écriture du monde arabe, qui s'étend de l'océan atlantique à l'océan pacifique. L'Abjad n'est pas un alphabet au sens européen du terme. Issue de calligraphies et rencontres culturelles variées, comment ce système d'écriture est-il devenu celui des arabes ? il n'est pas l'œuvre de Mahomet, il est plus ancien que le prophète. Mais le Coran demeure le livre sacré des arabes et l'Abjad, une écriture sacrée, intouchable, inviolable. La langue arabe a une morphologie particulière fondée sur des racines et des schèmes. Aussi, un mot peut être écrit sous un grand nombre de formes, sans compter les différentes orthographes dépendant de la prononciation car il existe de nombreux dialectes et un état de diglossie qui complique les règles d'usage dans le monde musulman, malgré un arabe classique, système présent dans le coran, et que tout homme éduqué doit savoir écrire et lire. L'Abjad n'est pas seulement un problème culturel, mais aussi un problème informatique. Unicode fut une révolution dans ce domaine, en permettant de traiter la totalité de l'Abjad avec ses 943 caractères environ, sur son unique page de code. Mais Unicode demande un encodage. Trois solutions alors sont proposées : utf-8, utf-16, utf-32. Nous avons retenu utf-32 malgré des inconvénients indiscutables. En somme, l'Abjad empêche, les arabes de développer à grande échelle la diffusion de texte dans leur langue. Il devrait donc, être possible de normaliser l'Abjad, mais pour des raisons religieuses, on ne peut retenir cette solution. Car, le monde arabe ne supporterait pas qu'en quelconque façon il soit porté atteinte à l'Abjad dans lequel le Coran est écrit. Mais cela même condamne l'Abjad à rester une écriture religieuse et non un outil de communication moderne pour les affaires, la banque et le commerce
Author: International Congress on Acoustics
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ISBN:
Category : Sound
Languages : en
Pages : 468
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Author:
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Category : Acoustical engineering
Languages : en
Pages : 476
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Author:
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Category : Sound
Languages : en
Pages : 466
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Author: Lucia Specia
Publisher: Springer Nature
ISBN: 3031021681
Category : Computers
Languages : en
Pages : 148
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Many applications within natural language processing involve performing text-to-text transformations, i.e., given a text in natural language as input, systems are required to produce a version of this text (e.g., a translation), also in natural language, as output. Automatically evaluating the output of such systems is an important component in developing text-to-text applications. Two approaches have been proposed for this problem: (i) to compare the system outputs against one or more reference outputs using string matching-based evaluation metrics and (ii) to build models based on human feedback to predict the quality of system outputs without reference texts. Despite their popularity, reference-based evaluation metrics are faced with the challenge that multiple good (and bad) quality outputs can be produced by text-to-text approaches for the same input. This variation is very hard to capture, even with multiple reference texts. In addition, reference-based metrics cannot be used in production (e.g., online machine translation systems), when systems are expected to produce outputs for any unseen input. In this book, we focus on the second set of metrics, so-called Quality Estimation (QE) metrics, where the goal is to provide an estimate on how good or reliable the texts produced by an application are without access to gold-standard outputs. QE enables different types of evaluation that can target different types of users and applications. Machine learning techniques are used to build QE models with various types of quality labels and explicit features or learnt representations, which can then predict the quality of unseen system outputs. This book describes the topic of QE for text-to-text applications, covering quality labels, features, algorithms, evaluation, uses, and state-of-the-art approaches. It focuses on machine translation as application, since this represents most of the QE work done to date. It also briefly describes QE for several other applications, including text simplification, text summarization, grammatical error correction, and natural language generation.
Author: Shay Cohen
Publisher: Springer Nature
ISBN: 3031021614
Category : Computers
Languages : en
Pages : 266
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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 for various shortcomings in the frequentist approach and to enrich it, especially in the unsupervised setting, where statistical learning is done without target prediction examples. 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. We also cover fundamental concepts in Bayesian statistics such as prior distributions, conjugacy, and generative modeling. Finally, we cover some of the fundamental modeling techniques in NLP, such as grammar modeling and their use with Bayesian analysis.
Author: Guy S. Goodwin-Gill
Publisher: Inter-Parliamentary Union
ISBN: 9291422770
Category : Election law
Languages : en
Pages : 230
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Author: Thierry Poibeau
Publisher: MIT Press
ISBN: 0262534215
Category : Computers
Languages : en
Pages : 298
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A concise, nontechnical overview of the development of machine translation, including the different approaches, evaluation issues, and major players in the industry. The dream of a universal translation device goes back many decades, long before Douglas Adams's fictional Babel fish provided this service in The Hitchhiker's Guide to the Galaxy. Since the advent of computers, research has focused on the design of digital machine translation tools—computer programs capable of automatically translating a text from a source language to a target language. This has become one of the most fundamental tasks of artificial intelligence. This volume in the MIT Press Essential Knowledge series offers a concise, nontechnical overview of the development of machine translation, including the different approaches, evaluation issues, and market potential. The main approaches are presented from a largely historical perspective and in an intuitive manner, allowing the reader to understand the main principles without knowing the mathematical details. The book begins by discussing problems that must be solved during the development of a machine translation system and offering a brief overview of the evolution of the field. It then takes up the history of machine translation in more detail, describing its pre-digital beginnings, rule-based approaches, the 1966 ALPAC (Automatic Language Processing Advisory Committee) report and its consequences, the advent of parallel corpora, the example-based paradigm, the statistical paradigm, the segment-based approach, the introduction of more linguistic knowledge into the systems, and the latest approaches based on deep learning. Finally, it considers evaluation challenges and the commercial status of the field, including activities by such major players as Google and Systran.
Author: Richard Abel
Publisher: Indiana University Press
ISBN: 9780253108708
Category : Performing Arts
Languages : en
Pages : 350
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The Sounds of Early Cinema is devoted exclusively to a little-known, yet absolutely crucial phenomenon: the ubiquitous presence of sound in early cinema. "Silent cinema" may rarely have been silent, but the sheer diversity of sound(s) and sound/image relations characterizing the first 20 years of moving picture exhibition can still astonish us. Whether instrumental, vocal, or mechanical, sound ranged from the improvised to the pre-arranged (as in scripts, scores, and cue sheets). The practice of mixing sounds with images differed widely, depending on the venue (the nickelodeon in Chicago versus the summer Chautauqua in rural Iowa, the music hall in London or Paris versus the newest palace cinema in New York City) as well as on the historical moment (a single venue might change radically, and many times, from 1906 to 1910). Contributors include Richard Abel, Rick Altman, Edouard Arnoldy, Mats Björkin, Stephen Bottomore, Marta Braun, Jean Châteauvert, Ian Christie, Richard Crangle, Helen Day-Mayer, John Fullerton, Jane Gaines, André Gaudreault, Tom Gunning, François Jost, Charlie Keil, Jeff Klenotic, Germain Lacasse, Neil Lerner, Patrick Loughney, David Mayer, Domi-nique Nasta, Bernard Perron, Jacques Polet, Lauren Rabinovitz, Isabelle Raynauld, Herbert Reynolds, Gregory A. Waller, and Rashit M. Yangirov.
