An introduction to Python programming for linguists. Examples of code specifically designed for language analysis are featured throughout.

"This manual is part of the official reference documentation for Python, an object-oriented programming language created by Guido van Rossum. Python is free software. The term “free software” refers to your freedom to run, copy, distribute, study, change and improve the software. With Python you have all these freedoms. You can support free software by becoming an associate member of the Free Software Foundation. The Free Software Foundation is a tax-exempt charity dedicated to promoting the right to use, study, copy, modify, and redistribute computer programs. It also helps to spread awareness of the ethical and political issues of freedom in the use of software. For more information visit the website www.fsf.org. The development of Python itself is supported by the Python Software Foundation. Companies using Python can invest in the language by becoming sponsoring members of this group. Donations can also be made online through the Python website. Further information is available at http://www.python.org/psf/."--Page 1.

Programming Linguistics examines a wide range of programming language designs, from Fortran to the newest research languages, to discover their common patterns, relationships, and antecedents. In studying the evolution of programming languages, the authors are also studying a series of answers to the central (and still unanswered) questions of what programs are and how they should be built. Programming Linguistics approaches language design as an attempt to define the nature of programming and the shape and structure of programs, rather than as the attempt to solve a series of narrow, disjoint technical problems. It emphasizes the structural-engineering rather than mathematical approach to programming, the importance of aesthetics and elegance in the success of language design, and provides an integrated treatment of concurrency and parallelism. Its readable and informal but rigorous coverage of the gamut of programming language designs is based on a simple and general programming model called the Ideal Software Machine. There are helpful exercises throughout. David Gelernter is an Associate Professor in the Department of Computer Science at Yale University. Suresh Jagannathan is an Associate Research Scientist at Yale.

This book is an introduction to the rudiments of Perl programming. It provides the general reader with an interest in language with the most usable and relevant aspects of Perl for writing programs that deal with language. Exposes the general reader with an interest in language to the most usable and relevant aspects of Perl for writing programs that deal with language. Contains simple examples and exercises that gradually introduce the reader to the essentials of good programming. Assumes no prior programming experience. Accompanied by exercises at the end of each chapter and offers all the code on the companion website: http://www.u.arizona.edu/~hammond

Statistics for Linguists: An Introduction Using R is the first statistics textbook on linear models for linguistics. The book covers simple uses of linear models through generalized models to more advanced approaches, maintaining its focus on conceptual issues and avoiding excessive mathematical details. It contains many applied examples using the R statistical programming environment. Written in an accessible tone and style, this text is the ideal main resource for graduate and advanced undergraduate students of Linguistics statistics courses as well as those in other fields, including Psychology, Cognitive Science, and Data Science.

A gentle introduction to programming for students and researchers interested in conducting computer-based analysis in linguistics, this book is an ideal starting point for linguists approaching programming for the first time. Assuming no background knowledge of programming, the author introduces basic notions and techniques needed for linguistics programming and helps readers to develop their understanding of electronic texts.The book includes many examples based on diverse topics in linguistics in order to demonstrate the applicability of the concepts at the heart of programming. Practical examples are designed to help the reader to:*Identify basic issues in handling language data, including Unicode processing*Conduct simple analyses in morphology/morphosyntax, and phonotactics*Understanding techniques for matching linguistic patterns*Learn to convert data into formats and data structures suitable for linguistic analysis*Create frequency lists from corpus materials to gather basic descriptive statistics on texts*Understand, obtain and 'clean up' web-based data*Design graphical user interfaces for writing more efficient and easy-to-use analysis tools.Two different types of exercise help readers to either learn to interpret and understand illustrative sample code, or to develop algorithmic thinking and solution strategies through turning a series of instructions into sample programs. Readers will be equipped with the necessary tools for designing their own extended projects.Key Features:*Ideal introduction for students of linguistics attempting to process corpus materials or literary texts for dissertations, theses or advanced research work*Linguistic examples throughout the text clearly demonstrate the application of programming theory and techniques*Coverage ranging from basic to more complex topics and methodologies enables the reader to progress at their own pace*Two chapters on the advantages of modularity and associated issues provid

This book offers a highly accessible introduction to natural language processing, the field that supports a variety of language technologies, from predictive text and email filtering to automatic summarization and translation. With it, you'll learn how to write Python programs that work with large collections of unstructured text. You'll access richly annotated datasets using a comprehensive range of linguistic data structures, and you'll understand the main algorithms for analyzing the content and structure of written communication. Packed with examples and exercises, Natural Language Processing with Python will help you: Extract information from unstructured text, either to guess the topic or identify "named entities" Analyze linguistic structure in text, including parsing and semantic analysis Access popular linguistic databases, including WordNet and treebanks Integrate techniques drawn from fields as diverse as linguistics and artificial intelligence This book will help you gain practical skills in natural language processing using the Python programming language and the Natural Language Toolkit (NLTK) open source library. If you're interested in developing web applications, analyzing multilingual news sources, or documenting endangered languages -- or if you're simply curious to have a programmer's perspective on how human language works -- you'll find Natural Language Processing with Python both fascinating and immensely useful.