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La problématique de cette thèse est l'extraction de connaissances à partir de données textuelles (KDT) en se basant sur la théorie des ensembles approximatifs (RST) et l'apprentissage symbolique et numérique. Les contributions sont : (1) l'extension des espaces de versions (espaces de versions approximatifs (RVS)), (2) l'application des RVS au KDT, (3) la découverte et visualisation de graphes à partir de textes. Tout d'abord, nous définissons les espaces de versions approximatifs (RVS), en construisant des opérateurs d'approximation, ce qui aboutit à un cadre général pour l'apprentissage symbolique automatique. L'introduction de la notion de consistance approximative conduit à l'utilisation de concepts presque consistants avec les données. En pratique, cela a pour effet d'étendre l'interprétation des concepts lors de l'apprentissage, et de traiter les données inconsistantes à l'aide de regroupement des exemples...
This book presents new and innovative ideas on the didactics of translation and interpreting. They include assessment methods and criteria, assessment of competences, graduate employability, placements, skills labs, the perceived skills gap between training and profession, the teaching of terminology, and curriculum design.
The effective use of educational assessments is fundamental to improving learning. However, effective use does not refer only to the technical parameters or statistical methodologies. Learning assessments in use todaywhether large-scale or household surveys or hybrid (smaller, quicker, cheaper or SQC)have varied uses and purposes. The present volume provides a review of learning assessments, their status in terms of the empirical knowledge base, and some new ideas for improving their effectiveness, particularly for those children most in need. It is argued here that SQC learning assessments have the potential to enhance educational accountability, increase transparency, and support a greater engagement of stakeholders with an interest in improving learning. In addition, countries need a sustained policy to guide assessment choices, including a focus on poor and marginalized populations.
Studies in Computational Linguistics presents authoritative texts from an international team of leading computational linguists. The books range from the senior undergraduate textbook to the research level monograph and provide a showcase for a broad range of recent developments in the field. The series should be interesting reading for researchers and students alike involved at this interface of linguistics and computing.
S'adressant à ceux qui souhaitent s'initier et s'entraîner, de manière raisonnée, à la pratique de la traduction, cet ouvrage offre une méthode d'analyse originale et efficace. La pratique observée est sans cesse présente sous forme d'exemples abondants et variés, les principes dégagés sont illustrés par des exercices. La démarche utilise les différents apports des sciences du langage en les subordonnant à la spécificité de l'acte de traduire. Les termes techniques sont chaque fois clairement définis. L'ordre suivi va du signe à l'énoncé, sans jamais perdre de vue les éléments constituants d'un ensemble qui est le texte. L'ouvrage représente un ensemble cohérent disposé avec ordre mais dont les éléments modulaires sont utilisables séparément, un système de renvois multiples permettant de mettre en perspective les chapitres.
The Early Grade Reading Assessment (EGRA) measures students' progress towards reading. EGRA gauges early literacy skills through a 15-minute individual oral assessment of five fundamental reading skills. RTI worked with education experts to develop the EGRA in 2006, and it has been piloted and implemented in more than 40 countries. This volume aims to take stock of the substantial amount of information and experience generated through the use of EGRA, and to share this knowledge with practitioners, policymakers, and international donors. Chapters cover not only particular applications of the instrument but also put EGRA in the context of broader issues and developments in literacy and education.
Africa is a huge continent with multicultural nations, where translation and interpretation are everyday occurrences. Translation studies has flourished in Africa in the last decade, with countries often having several official languages. The primary objective of this volume is to bring together research articles on translation and interpreting studies in Africa, written mainly, but not exclusively, by researchers living and working in the region. The focus is on the translation of literature and the media, and on the uses of interpreting. It provides a clear idea of the state and direction of research, and highlights research that is not commonly disseminated in North Africa and Europe. This book is an essential text for students and researchers working in translation studies, African studies and in African linguistics.
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.
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.