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This book is a comprehensive introduction to the statistical analysis of word frequency distributions, intended for computational linguists, corpus linguists, psycholinguists, and researchers in the field of quantitative stylistics. It aims to make these techniques more accessible for non-specialists, both theoretically, by means of a careful introduction to the underlying probabilistic and statistical concepts, and practically, by providing a program library implementing the main models for word frequency distributions.
This volume explores the universal mathematical properties underlying big language data and possible reasons why such properties exist, revealing how we may be unconsciously mathematical in our language use. These properties are statistical and thus different from linguistic universals that contribute to describing the variation of human languages, and they can only be identified over a large accumulation of usages. The book provides an overview of state-of-the art findings on these statistical universals and reconsiders the nature of language accordingly, with Zipf's law as a well-known example. The main focus of the book further lies in explaining the property of long memory, which was discovered and studied more recently by borrowing concepts from complex systems theory. The statistical universals not only possibly lie as the precursor of language system formation, but they also highlight the qualities of language that remain weak points in today's machine learning. In summary, this book provides an overview of language's global properties. It will be of interest to anyone engaged in fields related to language and computing or statistical analysis methods, with an emphasis on researchers and students in computational linguistics and natural language processing. While the book does apply mathematical concepts, all possible effort has been made to speak to a non-mathematical audience as well by communicating mathematical content intuitively, with concise examples taken from real texts.
Statistical analysis is a useful skill for linguists and psycholinguists, allowing them to understand the quantitative structure of their data. This textbook provides a straightforward introduction to the statistical analysis of language. Designed for linguists with a non-mathematical background, it clearly introduces the basic principles and methods of statistical analysis, using 'R', the leading computational statistics programme. The reader is guided step-by-step through a range of real data sets, allowing them to analyse acoustic data, construct grammatical trees for a variety of languages, quantify register variation in corpus linguistics, and measure experimental data using state-of-the-art models. The visualization of data plays a key role, both in the initial stages of data exploration and later on when the reader is encouraged to criticize various models. Containing over 40 exercises with model answers, this book will be welcomed by all linguists wishing to learn more about working with and presenting quantitative data.
The edited volume Sequences in Language and Text is the first collection of original research in the area of the quantitative analysis of sequentially organized linguistic data. Linguistic sequences are extremely useful textual structures in almost all areas of Language Technology. Character and word n-grams are by far the most successful features in text classification tasks such as authorship identification, text categorization, genre classification, sentiment analysis etc. Furthermore character linguistic sequences are the basis for linguistic modeling and subsequent applications such as speech recognition, language identification etc. In addition to the above language technology oriented research, the present volume aims to give insight to the theoretical value of linguistic sequences. Sequences in texts can be produced by a number of different factors, either external to the linguistic system or by its own grammatical structure. This volume hosts contributions which will analyze linguistic sequences using quantitative methods under the synergetic theoretical framework that can explain their role in the linguistic system.
The present book finds and collects absolutely new aspects of word frequency. First, eminent characteristics (such as the h-point, first used in scientometrics, the k-, m-, and n-points) are introduced – it can be shown that the geometry of word frequency is fundamentally based on them. Furthermore, various indicators of text properties are proposed for the first time, such as thematic concentration, autosemantic text compactness, autosemantic density, etc. In detail, the autosemantic structure of a given text is evaluated by means of a graph representation and its properties (according to a problem from network research). Special emphasis is given to the part-of-speech differentiation, which plays a significant role in stylistics. On the basis of a general theory, which has been developed especially for linguistic research, problems of the frequency structure of texts with respect to word occurrence are investigated and discussed in detail. Methodologically, specific reference is made to synergetic linguistics, including some exemplary analyses, showing that there are points of contact with this field. A separate chapter is dedicated to within-sentence word position; this issue considers grammar as well as language genesis; another chapter is dedicated to the type-token ratio, discussing all established methods and their relevance for word frequency analysis. All methods presented in the book are statistically tested; to this end, some new tests have been developed. All procedures and calculations are conducted for 20 languages, ranging from Polynesia, Indonesia, India, and Europe to a North American Indian language. The broad distribution of the data and texts from all genres allows generalizations with respect to language typology.
A comprehensive and accessible introduction to statistics in corpus linguistics, covering multiple techniques of quantitative language analysis and data visualisation.
Textbook on statistical analysis and data analysis - presents practical evaluation techniques, focusing on the computing and graphical fitting of regression. Bibliography after each chapter and statistical tables.
The edited volume Motifs in Language and Text is the first collection of original research in the area of the quantitative analysis of motifs. It hosts a collection of contributions that give insight to linguistic motifs theoretically across different languages, text genres, and structural levels, such as lexical, syntactic, semantic etc., and also to the tentative efforts upon the practical applications of the linguistic motifs. .
This collection of essays brings together many of the world's most distinguished statisticians to discuss a wide array of the most important recent developments in data analysis. The book honors John W. Tukey, one of the most influential statisticians of the twentieth century, on the occasion of his eightieth birthday. Contributors, some of them Tukey's former students, use his general theoretical work and his specific contributions to Exploratory Data Analysis as the point of departure for their papers. They cover topics from "pure" data analysis, such as gaussianizing transformations and regression estimates, and from "applied" subjects, such as the best way to rank the abilities of chess players or to estimate the abundance of birds in a particular area. Tukey may be best known for coining the common computer term "bit," for binary digit, but his broader work has revolutionized the way statisticians think about and analyze sets of data. In a personal interview that opens the book, he reviews these extraordinary contributions and his life with characteristic modesty, humor, and intelligence. The book will be valuable both to researchers and students interested in current theoretical and practical data analysis and as a testament to Tukey's lasting influence. The essays are by Dhammika Amaratunga, David Andrews, David Brillinger, Christopher Field, Leo Goodman, Frank Hampel, John Hartigan, Peter Huber, Mia Hubert, Clifford Hurvich, Karen Kafadar, Colin Mallows, Stephan Morgenthaler, Frederick Mosteller, Ha Nguyen, Elvezio Ronchetti, Peter Rousseeuw, Allan Seheult, Paul Velleman, Maria-Pia Victoria-Feser, and Alessandro Villa. Originally published in 1998. The Princeton Legacy Library uses the latest print-on-demand technology to again make available previously out-of-print books from the distinguished backlist of Princeton University Press. These editions preserve the original texts of these important books while presenting them in durable paperback and hardcover editions. The goal of the Princeton Legacy Library is to vastly increase access to the rich scholarly heritage found in the thousands of books published by Princeton University Press since its founding in 1905.