The Text In The Machine
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The Text in the Machine
The first comprehensive guide to explore the growing field of electronic information, The Text in the Machine: Electronic Texts in the Humanities will help you create and use electronic texts. This book explains the processes involved in developing computerized books on library Web sites, CD-ROMs, or your own Web site. With the information provided by The Text in the Machine, you?ll be able to successfully transfer written words to a digitized form and increase access to any kind of information. Keeping the perspectives of scholars, students, librarians, users, and publishers in mind, this book outlines the necessary steps for electronic conversion in a comprehensive manner. The Text in the Machine addresses many variables that need to be taken into consideration to help you digitize texts, such as: defining types of markup, markup systems, and their uses identifying characteristics of the written text, such as its linguistic and physical nature, before choosing a markup scheme ensuring accuracy in electronic texts by keying in information up to three times and choosing software that is compatible with the markup systems you are using examining the best file formats for scanning written texts and converting them to digital form explaining the delivery systems available for electronic texts, such as CD-ROMs, the Internet, magnetic tape, and the variety of software that will interpret these interfaces designing the structure of electronic texts with linear presentation, segmented text, or image files to increase readability and accessibility Containing lists of suggested readings and examples of electronic text Web sites, this book provides you with the opportunity to see how other libraries and scholars are creating and publishing digital texts. From The Text in the Machine, you?ll receive the knowledge to make this medium of information accessible and beneficial to patrons and scholars around the world.
Machine Learning for Text
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.
Supervised Machine Learning for Text Analysis in R
Text data is important for many domains, from healthcare to marketing to the digital humanities, but specialized approaches are necessary to create features for machine learning from language. Supervised Machine Learning for Text Analysis in R explains how to preprocess text data for modeling, train models, and evaluate model performance using tools from the tidyverse and tidymodels ecosystem. Models like these can be used to make predictions for new observations, to understand what natural language features or characteristics contribute to differences in the output, and more. If you are already familiar with the basics of predictive modeling, use the comprehensive, detailed examples in this book to extend your skills to the domain of natural language processing. This book provides practical guidance and directly applicable knowledge for data scientists and analysts who want to integrate unstructured text data into their modeling pipelines. Learn how to use text data for both regression and classification tasks, and how to apply more straightforward algorithms like regularized regression or support vector machines as well as deep learning approaches. Natural language must be dramatically transformed to be ready for computation, so we explore typical text preprocessing and feature engineering steps like tokenization and word embeddings from the ground up. These steps influence model results in ways we can measure, both in terms of model metrics and other tangible consequences such as how fair or appropriate model results are.