In The Essence of Linguistic Analysis by R. M. W. Dixon relates together, in a clear and succinct manner, individual grammatical categories, showing their dependencies and locating each in its place within the overall tapestry of a language.

Broad generalizations about "people today" are a familiar feature of first-year student writing. How Students Write brings a fresh perspective to this perennial observation, using corpus linguistics techniques. This study analyzes sentence-level patterns in student writing to develop an understanding of how students present evidence, draw connections between ideas, relate to their readers, and, ultimately, learn to construct knowledge in their writing. Drawing on both first-year and upper-level student writing, the book examines the discourse of students at different points in their education. It also distinguishes between argumentative and analytic essays to explore the way school genres and assignments shape students' choices. In focusing on sentence-level features such as hedges ("perhaps") and boosters ("definitely"), this study shows how such rhetorical choices work together to open or close opportunities for thoughtful exchanges of ideas. Attention to these features can help instructors foster civil discourse, design effective assignments, and expose and question norms of higher education.

The aim of this monograph is to give impetus to research into one of the central questions in discourse studies: what makes a sequence of sentences or utterances a discourse? The theoretical framework for describing the possibilities of discourse continuation is delineated by two principles: the discursive and the dialogic principle. The ‘chord’ of discourse is unfolded in a tripartite ‘wire’: Conjunction, Adjunction and Interjunction, each containing three aspects, leading to a Connectivity Model. This new three-by-three taxonomy of discourse relations incorporates findings from several theories and approaches that have evolved over the last three decades, including Systemic Functional Linguistics and Rhetorical Structure Theory. In comparing this model to other models, this book presents a state-of-the-art of discourse relation analysis combined with detailed accounts of many examples. This monograph furthermore proposes a new way of presenting discourse structures—in ‘connectivity graphs’—followed by eleven commandments for the segmentation and labeling of discourse, and three procedures for disambiguation if more labels are applicable. This study can provide a base for corpus linguistic analysis on discourse structures, computational approaches to discourse generation and cognitive experimental research of discourse competence.

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.

This handbook compares the main analytic frameworks and methods of contemporary linguistics. It offers a unique overview of linguistic theory, revealing the common concerns of competing approaches. By showing their current and potential applications it provides the means by which linguists and others can judge what are the most useful models for the task in hand. Distinguished scholars from all over the world explain the rationale and aims of over thirty explanatory approaches to the description, analysis, and understanding of language. Each chapter considers the main goals of the model; the relation it proposes from between lexicon, syntax, semantics, pragmatics, and phonology; the way it defines the interactions between cognition and grammar; what it counts as evidence; and how it explains linguistic change and structure. The Oxford Handbook of Linguistic Analysis offers an indispensable guide for everyone researching any aspect of language including those in linguistics, comparative philology, cognitive science, developmental philology, cognitive science, developmental psychology, computational science, and artificial intelligence. This second edition has been updated to include seven new chapters looking at linguistic units in language acquisition, conversation analysis, neurolinguistics, experimental phonetics, phonological analysis, experimental semantics, and distributional typology.