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A discourse analysis that is not based on grammar is likely to end up as a running commentary on a text, whereas a grammar-based one tends to treat text as a finished product rather than an on-going process. This book offers an approach to discourse analysis that is both grammar-based and oriented towards text as process. It proposes a model called TEXT TYPE within the framework of Hallidayan systemic-functional linguistics, which views grammatical choices in a text not as elements that combine to form a clause structure, but as semantic features that link successive clauses into an unfolding phase. It then demonstrates the model in actual analyses of 10 texts transcribed from 10 class hours' audio-recorded EFL classroom discourse, which in turn leads to the establishment of a dynamic system network that can be applied to future analyses of the process of EFL classroom discourse. The book also uncovers interesting details about EFL classroom teaching and learning in the Chinese context, including variations in the classroom environment, features of the interaction process, and discourse strategies of the teachers and students. It will be essential reading for academics and postgraduates working in the fields of discourse analysis, second language acquisition and systemic functional linguistics.
Natural language is one of the most important means of human communication. It enables us to express our will, to exchange thoughts and to document our knowledge in written sources. Owing to its substantial role in many facets of human life, technology for automatically analyzing and processing natural language has recently become increasingly important. In fact, natural language processing tools have paved the way for entirely new business opportunities. The goal of this book is to facilitate the automatic analysis of natural language in process models and to employ this analysis for assisting process model stakeholders. Therefore, a technique is defined that automatically recognizes and annotates process model element labels. In addition, this technique is leveraged to support organizations in effectively utilizing their process models in various ways. The book is organized into seven chapters. It starts with an overview of business process management and linguistics and continues with conceptual contributions on parsing and annotating process model elements, with the detection and correction of process model guideline violations, with the generation of natural language from process models and finally ends with the derivation of service candidates from process models.
This book constitutes the refereed proceedings of the 23rd International Conference on Advanced Information Systems Engineering, CAiSE 2011, held in London, UK, in June 2011. The 42 revised full papers and 5 revised short papers presented were carefully reviewed and selected from 320 submissions. In addtion the book contains the abstracts of 2 keynote speeches. The contributions are organized in topical sections on requirements; adaptation and evolution; model transformation; conceptual design; domain specific languages; case studies and experiences; mining and matching; business process modelling; validation and quality; and service and management.
Chapter 7. Case Study : Comparing Twitter Archives; Getting the Data and Distribution of Tweets; Word Frequencies; Comparing Word Usage; Changes in Word Use; Favorites and Retweets; Summary; Chapter 8. Case Study : Mining NASA Metadata; How Data Is Organized at NASA; Wrangling and Tidying the Data; Some Initial Simple Exploration; Word Co-ocurrences and Correlations; Networks of Description and Title Words; Networks of Keywords; Calculating tf-idf for the Description Fields; What Is tf-idf for the Description Field Words?; Connecting Description Fields to Keywords; Topic Modeling.
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.
Writing matters, and so does research into real-life writing. The shift from an industrial to an information society has increased the importance of writing and text production in education, in everyday life and in more and more professions in the fields of economics and politics, science and technology, culture and media. Through writing, we build up organizations and social networks, develop projects, inform colleagues and customers, and generate the basis for decisions. The quality of writing is decisive for social resonance and professional success. This ubiquitous real-life writing is what the present handbook is about. The de Gruyter Handbook of Writing and Text Production brings together and systematizes state-of-the-art research. The volume contains five sections, focussing on (I) the theory and methodology of writing and text production research, as well as on problem-oriented and problem-solving approaches related to (II) authors, (III) modes and media, (IV) genres, and (V) domains of writing and text production. Throughout the 21 chapters, exemplary research projects illustrate the theoretical perspectives from globally relevant research spaces and traditions. Both established and future scholars can benefit from the handbook’s fresh approach to writing in the context of multimodal, multi-semiotic text production.
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
What is text understanding? It is the dynamic process of constructing coherent representations and inferences at multiple levels of text and context, within the bottleneck of a limited-capacity working memory. The field of text and discourse has advanced to the point where researchers have developed sophisticated models of comprehension, and identified the particular assumptions that underlie comprehension mechanisms in precise analytical or mathematical detail. The models offer a priori predictions about thought and behavior, not merely ad hoc descriptions of data. Indeed, the field has evolved to a mature science. The contributors to this volume collectively cover the major models of comprehension in the field of text and discourse. Other books are either narrow -- covering only a single theoretical framework -- or do not focus on systematic modeling efforts. In addition, this book focuses on deep levels of understanding rather than language codes, syntax, and other shallower levels of text analysis. As such, it provides readers with up-to-date information on current psychological models specified in quantitative or analytical detail.
Extract actionable insights from text and unstructured data. Information extraction is the task of automatically extracting structured information from unstructured or semi-structured text. SAS Text Analytics for Business Applications: Concept Rules for Information Extraction Models focuses on this key element of natural language processing (NLP) and provides real-world guidance on the effective application of text analytics. Using scenarios and data based on business cases across many different domains and industries, the book includes many helpful tips and best practices from SAS text analytics experts to ensure fast, valuable insight from your textual data. Written for a broad audience of beginning, intermediate, and advanced users of SAS text analytics products, including SAS Visual Text Analytics, SAS Contextual Analysis, and SAS Enterprise Content Categorization, this book provides a solid technical reference. You will learn the SAS information extraction toolkit, broaden your knowledge of rule-based methods, and answer new business questions. As your practical experience grows, this book will serve as a reference to deepen your expertise.
This compact and original reference and textbook presents the most important classical and modern essentials of control engineering in a single volume. It constitutes a harmonic mixture of control theory and applications, which makes the book especially useful for students, practicing engineers and researchers interested in modeling and control of processes. Well written and easily understandable, it includes a range of methods for the analysis and design of control systems.