Formal Models In The Study Of Language
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Formal Models in the Study of Language
This volume presents articles that focus on the application of formal models in the study of language in a variety of innovative ways, and is dedicated to Jacques Moeschler, professor at University of Geneva, to mark the occasion of his 60th birthday. The contributions, by seasoned and budding linguists of all different linguistic backgrounds, reflect Jacques Moeschler’s diverse and visionary research over the years. The book contains three parts. The first part shows how different formal models can be applied to the analysis of such diverse problems as the syntax, semantics and pragmatics of tense, aspect and deictic expressions, syntax and pragmatics of quantifiers and semantics and pragmatics of connectives and negation. The second part presents the application of formal models to the treatment of cognitive issues related to the use of language, and in particular, demonstrating cognitive accounts of different types of human interactions, the context in utterance interpretation (salience, inferential comprehension processes), figurative uses of language (irony pretence), the role of syntax in Theory of Mind in autism and the analysis of the aesthetics of nature. Finally, the third part addresses computational and corpus-based approaches to natural language for investigating language variation, language universals and discourse related issues. This volume will be of great interest to syntacticians, pragmaticians, computer scientists, semanticians and psycholinguists.
Theory of Formal Languages with Applications
Formal languages provide the theoretical underpinnings for the study of programming languages as well as the foundations for compiler design. They are important in such areas as data transmission and compression, computer networks, etc. This book combines an algebraic approach with algorithmic aspects and decidability results and explores applications both within computer science and in fields where formal languages are finding new applications such as molecular and developmental biology. It contains more than 600 graded exercises. While some are routine, many of the exercises are in reality supplementary material. Although the book has been designed as a text for graduate and upper-level undergraduate students, the comprehensive coverage of the subject makes it suitable as a reference for scientists.
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.