A spirited study of a neglected topic, these essays explore the character and uses of annotation from Biblical times to the present. A group of distinguished scholars investigates such subjects as the bullying footnote, the play of note against text, the self-annotation of the Bible, the parasitical commentator, the note as imperial seal, the agonies of modern scholarly publication, the hidden marginalium, and the ways in which supplements to the text tend to push aside the text. Casting light on a matter which readers usually ignore, this witty, readable, and revisionist book offers a provocative invitation for further discussion.
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.
Linguistic annotation and text analytics are active areas of research and development, with academic conferences and industry events such as the Linguistic Annotation Workshops and the annual Text Analytics Summits. This book provides a basic introduction to both fields, and aims to show that good linguistic annotations are the essential foundation for good text analytics. After briefly reviewing the basics of XML, with practical exercises illustrating in-line and stand-off annotations, a chapter is devoted to explaining the different levels of linguistic annotations. The reader is encouraged to create example annotations using the WordFreak linguistic annotation tool. The next chapter shows how annotations can be created automatically using statistical NLP tools, and compares two sets of tools, the OpenNLP and Stanford NLP tools. The second half of the book describes different annotation formats and gives practical examples of how to interchange annotations between different formats using XSLT transformations. The two main text analytics architectures, GATE and UIMA, are then described and compared, with practical exercises showing how to configure and customize them. The final chapter is an introduction to text analytics, describing the main applications and functions including named entity recognition, coreference resolution and information extraction, with practical examples using both open source and commercial tools. Copies of the example files, scripts, and stylesheets used in the book are available from the companion website, located at http: //sites.morganclaypool.com/wilcock. Table of Contents: Working with XML / Linguistic Annotation / Using Statistical NLP Tools / Annotation Interchange / Annotation Architectures / Text Analytics
This handbook offers a thorough treatment of the science of linguistic annotation. Leaders in the field guide the reader through the process of modeling, creating an annotation language, building a corpus and evaluating it for correctness. Essential reading for both computer scientists and linguistic researchers.Linguistic annotation is an increasingly important activity in the field of computational linguistics because of its critical role in the development of language models for natural language processing applications. Part one of this book covers all phases of the linguistic annotation process, from annotation scheme design and choice of representation format through both the manual and automatic annotation process, evaluation, and iterative improvement of annotation accuracy. The second part of the book includes case studies of annotation projects across the spectrum of linguistic annotation types, including morpho-syntactic tagging, syntactic analyses, a range of semantic analyses (semantic roles, named entities, sentiment and opinion), time and event and spatial analyses, and discourse level analyses including discourse structure, co-reference, etc. Each case study addresses the various phases and processes discussed in the chapters of part one.
Corpus Annotation gives an up-to-date picture of this fascinating new area of research, and will provide essential reading for newcomers to the field as well as those already involved in corpus annotation. Early chapters introduce the different levels and techniques of corpus annotation. Later chapters deal with software developments, applications, and the development of standards for the evaluation of corpus annotation. While the book takes detailed account of research world-wide, its focus is particularly on the work of the UCREL (University Centre for Computer Corpus Research on Language) team at Lancaster University, which has been at the forefront of developments in the field of corpus annotation since its beginnings in the 1970s.
In this new edition of their groundbreaking book Strategies That Work, Stephanie Harvey and Anne Goudvis share the work and thinking they've done since the second edition came out a decade ago and offer new perspectives on how to explicitly teach thinking strategies so that students become engaged, thoughtful, independent readers. Thirty new lessons and new and revised chapters shine a light on children's thinking, curiosity, and questions. Steph and Anne tackle close reading, close listening, text complexity, and critical thinking in a new chapter on building knowledge through thinking-intensive reading and learning. Other fully revised chapters focus on digital reading, strategies for integrating comprehension and technology, and comprehension across the curriculum. The new edition is organized around three sections: Part I provides readers with a solid introduction to reading comprehension instruction, including the principles that guide practice, suggestions for text selection, and a review of recent research that underlies comprehension instruction. Part II contains lessons to put these principles into practice for all areas of reading comprehension. Part III shows you how to integrate comprehension instruction across the curriculum and the school day, particularly in science and social studies. Updated bibliographies, including the popular "Great Books for Teaching Content," are accessible online. Since the first publication of Strategies That Work, more than a million teachers have benefited from Steph and Anne's practical advice on creating classrooms that are incubators for deep thought. This third edition is a must-have resource for a generation of new teachers--and a welcome refresher for those with dog-eared copies of this timeless guide to teaching comprehension.
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.
For many researchers, Python is a first-class tool mainly because of its libraries for storing, manipulating, and gaining insight from data. Several resources exist for individual pieces of this data science stack, but only with the Python Data Science Handbook do you get them all—IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and other related tools. Working scientists and data crunchers familiar with reading and writing Python code will find this comprehensive desk reference ideal for tackling day-to-day issues: manipulating, transforming, and cleaning data; visualizing different types of data; and using data to build statistical or machine learning models. Quite simply, this is the must-have reference for scientific computing in Python. With this handbook, you’ll learn how to use: IPython and Jupyter: provide computational environments for data scientists using Python NumPy: includes the ndarray for efficient storage and manipulation of dense data arrays in Python Pandas: features the DataFrame for efficient storage and manipulation of labeled/columnar data in Python Matplotlib: includes capabilities for a flexible range of data visualizations in Python Scikit-Learn: for efficient and clean Python implementations of the most important and established machine learning algorithms
Linguistic annotation and text analytics are active areas of research and development, with academic conferences and industry events such as the Linguistic Annotation Workshops and the annual Text Analytics Summits. This book provides a basic introduction to both fields, and aims to show that good linguistic annotations are the essential foundation for good text analytics. After briefly reviewing the basics of XML, with practical exercises illustrating in-line and stand-off annotations, a chapter is devoted to explaining the different levels of linguistic annotations. The reader is encouraged to create example annotations using the WordFreak linguistic annotation tool. The next chapter shows how annotations can be created automatically using statistical NLP tools, and compares two sets of tools, the OpenNLP and Stanford NLP tools. The second half of the book describes different annotation formats and gives practical examples of how to interchange annotations between different formats using XSLT transformations. The two main text analytics architectures, GATE and UIMA, are then described and compared, with practical exercises showing how to configure and customize them. The final chapter is an introduction to text analytics, describing the main applications and functions including named entity recognition, coreference resolution and information extraction, with practical examples using both open source and commercial tools. Copies of the example files, scripts, and stylesheets used in the book are available from the companion website, located at the book website. Table of Contents: Working with XML / Linguistic Annotation / Using Statistical NLP Tools / Annotation Interchange / Annotation Architectures / Text Analytics
