Information, Knowledge, Text is concerned with connections between computing and writing and precursors to modern information technologies. It brings historical and humanistic perspectives to bear on contemporary information developments, enabling a deepening understanding of those developments. Rather than developing a single overarching thesis, Warner weaves together several themes, basing his chapters on carefully edited journal articles and conference presentations. Individual essays cover the history of writing and signal transmission, the concept of exactness as it relates to human semiotic constructions, forms of representation in formal logic and automata studies, copyright, and graphic communication. A final chapter offers a review of literature that further explores the established themes.

The scope of this volume will encompass a collection of research papers related to indexing and retrieval of online non-text information. In recent years, the Internet has seen an exponential increase in the number of documents placed online that are not in textual format. These documents appear in a variety of contexts, such as user-generated content sharing websites, social networking websites etc. and formats, including photographs, videos, recorded music, data visualizations etc. The prevalence of these contexts and data formats presents a particularly challenging task to information indexing and retrieval research due to many difficulties, such as assigning suitable semantic metadata, processing and extracting non-textual content automatically, and designing retrieval systems that "speak in the native language" of non-text documents.

A guide for using computational text analysis to learn about the social world From social media posts and text messages to digital government documents and archives, researchers are bombarded with a deluge of text reflecting the social world. This textual data gives unprecedented insights into fundamental questions in the social sciences, humanities, and industry. Meanwhile new machine learning tools are rapidly transforming the way science and business are conducted. Text as Data shows how to combine new sources of data, machine learning tools, and social science research design to develop and evaluate new insights. Text as Data is organized around the core tasks in research projects using text—representation, discovery, measurement, prediction, and causal inference. The authors offer a sequential, iterative, and inductive approach to research design. Each research task is presented complete with real-world applications, example methods, and a distinct style of task-focused research. Bridging many divides—computer science and social science, the qualitative and the quantitative, and industry and academia—Text as Data is an ideal resource for anyone wanting to analyze large collections of text in an era when data is abundant and computation is cheap, but the enduring challenges of social science remain. Overview of how to use text as data Research design for a world of data deluge Examples from across the social sciences and industry

This book develops concise and comprehensive concepts for extracting degree information from natural language texts. First, an overview of the ParseTalk information extraction system is given. Then, from the review of relevant linguistic literature, the author derives two distinct categories of natural language degree expressions and proposes knowledge-intensive algorithms to handle their analyses in the ParseTalk system. Moreover, for inferencing the author generalizes from well-known constraint propagation mechanisms. The concepts and methods developed are applied to text domains from medical diagnosis and information technology magazines. The conclusion of the book gives an integration of all three levels of understanding resulting in more advanced and more efficient information extraction mechanisms.

formation. The basic ideas underlying knowledge visualization and information vi- alization are outlined. In a short preview of the contributions of this volume, the idea behind each approach and its contribution to the goals of the book are outlined. 2 The Basic Concepts of the Book Three basic concepts are the focus of this book: "data", "information", and "kno- edge". There have been numerous attempts to define the terms "data", "information", and "knowledge", among them, the OTEC Homepage "Data, Information, Kno- edge, and Wisdom" (Bellinger, Castro, & Mills, see http://www.syste- thinking.org/dikw/dikw.htm): Data are raw. They are symbols or isolated and non-interpreted facts. Data rep- sent a fact or statement of event without any relation to other data. Data simply exists and has no significance beyond its existence (in and of itself). It can exist in any form, usable or not. It does not have meaning of itself.

This book explores the relationship between theory and practice in ancient Greek and Roman scientific and technical texts.

The Definitive Resource on Text Mining Theory and Applications from Foremost Researchers in the FieldGiving a broad perspective of the field from numerous vantage points, Text Mining: Classification, Clustering, and Applications focuses on statistical methods for text mining and analysis. It examines methods to automatically cluster and classify te

The promise of the Semantic Web is that future web pages will be annotated not only with bright colors and fancy fonts as they are now, but with annotation extracted from large domain ontologies that specify, to a computer in a way that it can exploit, what information is contained on the given web page. The presence of this information will allow software agents to examine pages and to make decisions about content as humans are able to do now. The classic method of building an ontology is to gather a committee of experts in the domain to be modeled by the ontology, and to have this committee.

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.

There are many reasons to be curious about the way people learn, and the past several decades have seen an explosion of research that has important implications for individual learning, schooling, workforce training, and policy. In 2000, How People Learn: Brain, Mind, Experience, and School: Expanded Edition was published and its influence has been wide and deep. The report summarized insights on the nature of learning in school-aged children; described principles for the design of effective learning environments; and provided examples of how that could be implemented in the classroom. Since then, researchers have continued to investigate the nature of learning and have generated new findings related to the neurological processes involved in learning, individual and cultural variability related to learning, and educational technologies. In addition to expanding scientific understanding of the mechanisms of learning and how the brain adapts throughout the lifespan, there have been important discoveries about influences on learning, particularly sociocultural factors and the structure of learning environments. How People Learn II: Learners, Contexts, and Cultures provides a much-needed update incorporating insights gained from this research over the past decade. The book expands on the foundation laid out in the 2000 report and takes an in-depth look at the constellation of influences that affect individual learning. How People Learn II will become an indispensable resource to understand learning throughout the lifespan for educators of students and adults.