The series serves to propagate investigations into language usage, especially with respect to computational support. This includes all forms of text handling activity, not only interlingual translations, but also conversions carried out in response to different communicative tasks. Among the major topics are problems of text transfer and the interplay between human and machine activities.
Natural Language is not only the most important means of communication between human beings, it is also used over historical periods for the pres- vation of cultural achievements and their transmission from one generation to the other. During the last few decades, the ?ood of digitalized information has been growing tremendously. This tendency will continue with the globali- tion of information societies and with the growing importance of national and international computer networks. This is one reason why the theoretical und- standing and the automated treatment of communication processes based on natural language have such a decisive social and economic impact. In this c- text, the semantic representation of knowledge originally formulated in natural language plays a central part, because it connects all components of natural language processing systems, be they the automatic understanding of natural language (analysis), the rational reasoning over knowledge bases, or the g- eration of natural language expressions from formal representations. This book presents a method for the semantic representation of natural l- guage expressions (texts, sentences, phrases, etc. ) which can be used as a u- versal knowledge representation paradigm in the human sciences, like lingu- tics, cognitive psychology, or philosophy of language, as well as in com- tational linguistics and in arti?cial intelligence. It is also an attempt to close the gap between these disciplines, which to a large extent are still working separately.
Suitable for students, researchers and scholars who need to learn how to read and translate modern Dutch texts for their academic research, this book focuses on those areas where the Netherlands plays or has played a leading and innovative role in the world.
The book seeks to enlarge the theoretical scope, research agenda, and practices associated with TBLT in a two-way dynamic, by exploring how insights from writing might reconfigure our understanding of tasks and, in turn, how work associated with TBLT might benefit the learning and teaching of writing. In order to enrich the domain of task and to advance the educational interests of TBLT, it adopts both a psycholinguistic and a textual meaning-making orientation. Following an issues-oriented introductory chapter, Part I of the volume explores tenets, methods, and findings in task-oriented theory and research in the context of writing; the chapters in Part II present empirical findings on task-based writing by investigating how writing tasks are implemented, how writers differentially respond to tasks, and how tasks can contribute to language development. A coda chapter summarizes the volume’s contribution and suggests directions for advancing TBLT constructs and research agendas.
The editors of WRITING IN KNOWLEDGE SOCIETIES provide a thoughtful, carefully constructed collection that addresses the vital roles rhetoric and writing play as knowledge-making practices in diverse knowledge-intensive settings. The essays in this book examine the multiple, subtle, yet consequential ways in which writing is epistemic, articulating the central role of writing in creating, shaping, sharing, and contesting knowledge in a range of human activities in workplaces, civic settings, and higher education.
Text analytics is a field that lies on the interface of information retrieval,machine learning, and natural language processing, and this textbook carefully covers a coherently organized framework drawn from these intersecting topics. The chapters of this textbook is organized into three categories: - Basic algorithms: Chapters 1 through 7 discuss the classical algorithms for machine learning from text such as preprocessing, similarity computation, topic modeling, matrix factorization, clustering, classification, regression, and ensemble analysis. - Domain-sensitive mining: Chapters 8 and 9 discuss the learning methods from text when combined with different domains such as multimedia and the Web. The problem of information retrieval and Web search is also discussed in the context of its relationship with ranking and machine learning methods. - Sequence-centric mining: Chapters 10 through 14 discuss various sequence-centric and natural language applications, such as feature engineering, neural language models, deep learning, text summarization, information extraction, opinion mining, text segmentation, and event detection. This textbook covers machine learning topics for text in detail. Since the coverage is extensive,multiple courses can be offered from the same book, depending on course level. Even though the presentation is text-centric, Chapters 3 to 7 cover machine learning algorithms that are often used indomains beyond text data. Therefore, the book can be used to offer courses not just in text analytics but also from the broader perspective of machine learning (with text as a backdrop). This textbook targets graduate students in computer science, as well as researchers, professors, and industrial practitioners working in these related fields. This textbook is accompanied with a solution manual for classroom teaching.
Rothkegel argues that text production is the result of interaction between text knowledge and object knowledge – the conventional ordering and presentation of knowledge for communicative purposes and the conceptual organisation of world knowledge.
Language in Use creatively brings together, for the first time, perspectives from cognitive linguistics, language acquisition, discourse analysis, and linguistic anthropology. The physical distance between nations and continents, and the boundaries between different theories and subfields within linguistics have made it difficult to recognize the possibilities of how research from each of these fields can challenge, inform, and enrich the others. This book aims to make those boundaries more transparent and encourages more collaborative research. The unifying theme is studying how language is used in context and explores how language is shaped by the nature of human cognition and social-cultural activity. Language in Use examines language processing and first language learning and illuminates the insights that discourse and usage-based models provide in issues of second language learning. Using a diverse array of methodologies, it examines how speakers employ various discourse-level resources to structure interaction and create meaning. Finally, it addresses issues of language use and creation of social identity. Unique in approach and wide-ranging in application, the contributions in this volume place emphasis on the analysis of actual discourse and the insights that analyses of such data bring to language learning as well as how language shapes and reflects social identity—making it an invaluable addition to the library of anyone interested in cutting-edge linguistics.
This book provides a perspective on the application of machine learning-based methods in knowledge discovery from natural languages texts. By analysing various data sets, conclusions which are not normally evident, emerge and can be used for various purposes and applications. The book provides explanations of principles of time-proven machine learning algorithms applied in text mining together with step-by-step demonstrations of how to reveal the semantic contents in real-world datasets using the popular R-language with its implemented machine learning algorithms. The book is not only aimed at IT specialists, but is meant for a wider audience that needs to process big sets of text documents and has basic knowledge of the subject, e.g. e-mail service providers, online shoppers, librarians, etc. The book starts with an introduction to text-based natural language data processing and its goals and problems. It focuses on machine learning, presenting various algorithms with their use and possibilities, and reviews the positives and negatives. Beginning with the initial data pre-processing, a reader can follow the steps provided in the R-language including the subsuming of various available plug-ins into the resulting software tool. A big advantage is that R also contains many libraries implementing machine learning algorithms, so a reader can concentrate on the principal target without the need to implement the details of the algorithms her- or himself. To make sense of the results, the book also provides explanations of the algorithms, which supports the final evaluation and interpretation of the results. The examples are demonstrated using realworld data from commonly accessible Internet sources.
