An introduction to annotation as a genre--a synthesis of reading, thinking, writing, and communication--and its significance in scholarship and everyday life. Annotation--the addition of a note to a text--is an everyday and social activity that provides information, shares commentary, sparks conversation, expresses power, and aids learning. It helps mediate the relationship between reading and writing. This volume in the MIT Press Essential Knowledge series offers an introduction to annotation and its literary, scholarly, civic, and everyday significance across historical and contemporary contexts. It approaches annotation as a genre--a synthesis of reading, thinking, writing, and communication--and offer examples of annotation that range from medieval rubrication and early book culture to data labeling and online reviews.

The Not-So-Dark Dark Ages What they forgot to teach you in school: People in the Middle Ages did not think the world was flat The Inquisition never executed anyone because of their scientific ideologies It was medieval scientific discoveries, including various methods, that made possible Western civilization’s “Scientific Revolution” As a physicist and historian of science James Hannam debunks myths of the Middle Ages in his brilliant book The Genesis of Science: How the Christian Middle Ages Launched the Scientific Revolution. Without the medieval scholars, there would be no modern science. Discover the Dark Ages and their inventions, research methods, and what conclusions they actually made about the shape of the world.

From the editor of the popular Annotated Pride and Prejudice comes an annotated edition of Jane Austen’s Emma that makes her beloved tale of an endearingly inept matchmaker an even more satisfying read. Here is the complete text of the novel with more than 2,200 annotations on facing pages, including: - Explanations of historical context - Citations from Austen’s life, letters, and other writings - Definitions and clarifications - Literary comments and analysis - Maps of places in the novel - An introduction, bibliography, and detailed chronology of events - Nearly 200 informative illustrations Filled with fascinating information about everything from the social status of spinsters and illegitimate children to the shopping habits of fashionable ladies to English attitudes toward gypsies, David M. Shapard’s Annotated Emma brings Austen’s world into richer focus.

Annotations in modern books are a phenomenon that often causes disapproval: we are not supposed to draw, doodle, underline, or highlight in our books. In many medieval manuscripts, however, the pages are filled with annotations around the text and in-between the lines. In some cases, a 'white space' around the text is even laid out to contain extra text, pricked and ruled for the purpose. Just as footnotes are an approved and standard part of the modern academic book, so the flyleaves, margins, and interlinear spaces of many medieval manuscripts are an invitation to add extra text. This volume focuses on annotation in the early medieval period. In treating manuscripts as mirrors of the medieval minds who created them - reflecting their interests, their choices, their practices - the essays explore a number of key topics. Are there certain genres in which the making of annotations seems to be more appropriate or common than in others? Are there genres in which annotating is 'not done'? Are there certain monastic centres in which annotating practices flourish, and from which they spread? The volume thus investigates whether early medieval annotators used specific techniques, perhaps identifiable with their scribal communities or schools. It explores what annotators actually sought to accomplish with their annotations, and how the techniques of annotating developed over time and per region.

This gifty book journal offers the perfect place to indulge your passion for reading and to record your literary explorations. This beautifully refreshed edition of our popular My Book Journal provides space to record reviews and thoughts on 100 books, as well as track star ratings for quality of writing, strength of characters, and plot. It also includes 24 enlightening book challenges, book-club questions, and a classics section with must-read titles. Plus, you can fill out 24 thought-provoking lists--from your top 10 favorite characters to your favorite childhood books--and you'll find complete lists of Pulitzer Prize and Booker Prize winners.

Helps student to understand himself as a learner and what it takes to succeed. Focuses on four key factors; Students characteristics as learners; the tasks which must be completed in each class; the strategies that will help the student to read, understand and remember what professors expect him to learn and the texts with which the student interact.

Create your own natural language training corpus for machine learning. Whether you’re working with English, Chinese, or any other natural language, this hands-on book guides you through a proven annotation development cycle—the process of adding metadata to your training corpus to help ML algorithms work more efficiently. You don’t need any programming or linguistics experience to get started. Using detailed examples at every step, you’ll learn how the MATTER Annotation Development Process helps you Model, Annotate, Train, Test, Evaluate, and Revise your training corpus. You also get a complete walkthrough of a real-world annotation project. Define a clear annotation goal before collecting your dataset (corpus) Learn tools for analyzing the linguistic content of your corpus Build a model and specification for your annotation project Examine the different annotation formats, from basic XML to the Linguistic Annotation Framework Create a gold standard corpus that can be used to train and test ML algorithms Select the ML algorithms that will process your annotated data Evaluate the test results and revise your annotation task Learn how to use lightweight software for annotating texts and adjudicating the annotations This book is a perfect companion to O’Reilly’s Natural Language Processing with Python.