Word storage and processing define a multi-factorial domain of scientific inquiry whose thorough investigation goes well beyond the boundaries of traditional disciplinary taxonomies, to require synergic integration of a wide range of methods, techniques and empirical and experimental findings. The present book intends to approach a few central issues concerning the organization, structure and functioning of the Mental Lexicon, by asking domain experts to look at common, central topics from complementary standpoints, and discuss the advantages of developing converging perspectives. The book will explore the connections between computational and algorithmic models of the mental lexicon, word frequency distributions and information theoretical measures of word families, statistical correlations across psycho-linguistic and cognitive evidence, principles of machine learning and integrative brain models of word storage and processing. Main goal of the book will be to map out the landscape of future research in this area, to foster the development of interdisciplinary curricula and help single-domain specialists understand and address issues and questions as they are raised in other disciplines.

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.

This new edition to the classic book by ggplot2 creator Hadley Wickham highlights compatibility with knitr and RStudio. ggplot2 is a data visualization package for R that helps users create data graphics, including those that are multi-layered, with ease. With ggplot2, it's easy to:
produce handsome, publication-quality plots with automatic legends created from the plot specification
superimpose multiple layers (points, lines, maps, tiles, box plots) from different data sources with automatically adjusted common scales
add customizable smoothers that use powerful modeling capabilities of R, such as loess, linear models, generalized additive models, and robust regression
save any ggplot2 plot (or part thereof) for later modification or reuse
create custom themes that capture in-house or journal style requirements and that can easily be applied to multiple plots
approach a graph from a visual perspective, thinking about how each component of the data is represented on the final plot
This book will be useful to everyone who has struggled with displaying data in an informative and attractive way. Some basic knowledge of R is necessary (e.g., importing data into R). ggplot2 is a mini-language specifically tailored for producing graphics, and you'll learn everything you need in the book. After reading this book you'll be able to produce graphics customized precisely for your problems, and you'll find it easy to get graphics out of your head and on to the screen or page.

Providing a much-needed bridge between elementary statistics courses and advanced research methods courses, Understanding Advanced Statistical Methods helps students grasp the fundamental assumptions and machinery behind sophisticated statistical topics, such as logistic regression, maximum likelihood, bootstrapping, nonparametrics, and Bayesian methods. The book teaches students how to properly model, think critically, and design their own studies to avoid common errors. It leads them to think differently not only about math and statistics but also about general research and the scientific method. With a focus on statistical models as producers of data, the book enables students to more easily understand the machinery of advanced statistics. It also downplays the "population" interpretation of statistical models and presents Bayesian methods before frequentist ones. Requiring no prior calculus experience, the text employs a "just-in-time" approach that introduces mathematical topics, including calculus, where needed. Formulas throughout the text are used to explain why calculus and probability are essential in statistical modeling. The authors also intuitively explain the theory and logic behind real data analysis, incorporating a range of application examples from the social, economic, biological, medical, physical, and engineering sciences. Enabling your students to answer the why behind statistical methods, this text teaches them how to successfully draw conclusions when the premises are flawed. It empowers them to use advanced statistical methods with confidence and develop their own statistical recipes. Ancillary materials are available on the book's website.

The twenty-first century has seen a breathtaking expansion of statistical methodology, both in scope and in influence. 'Big data', 'data science', and 'machine learning' have become familiar terms in the news, as statistical methods are brought to bear upon the enormous data sets of modern science and commerce. How did we get here? And where are we going? This book takes us on an exhilarating journey through the revolution in data analysis following the introduction of electronic computation in the 1950s. Beginning with classical inferential theories - Bayesian, frequentist, Fisherian - individual chapters take up a series of influential topics: survival analysis, logistic regression, empirical Bayes, the jackknife and bootstrap, random forests, neural networks, Markov chain Monte Carlo, inference after model selection, and dozens more. The distinctly modern approach integrates methodology and algorithms with statistical inference. The book ends with speculation on the future direction of statistics and data science.
Clarifies both traditional methods and current, popular algorithms (e.g. neural nets, random forests)
Written by two world-leading researchers
Addressed to all fields that work with data

本书的特点是，在介绍各种实验设计原理的基础上，将实验设计、统计分析和计算机数据处理三方面内容紧密结合，通过大量举例，对从如何根据研究课题进行实验设计，如何进行方差分析，如何得出研究结论做了详细的介绍...
使用多因素实验设计是当前心理和教育研究发展的趋势。它可在一定程度上克服早期实验室和教育现场研究的局限性，使实验研究更加深入，探索更加复杂的现象，同时使研究结果更加精确。实验设计也是一门技术，它包括实验设计、统计分析和计算机数据处理三方面的知识，缺一不可。本书的特点是，在介绍各种实验设计原理的基础上，将实验设计、统计分析和计算机数据处理三方面内容紧密结合，通过大量举例，对从如何根据研究课题进行实验设计，如何进行方差分析，如何得出研究结论做了详细的介绍，并且介绍了如何编制sPSs方差分析程序对书中的例题进行数据处理，和阅读输出结果。因而，可使读者较好地把三方面知识结合起来，较快地掌握实验设计的原理与操作，用于自己的研究。本书的内容在作者几次给本科生、研究生开课中收到很好的效果。
本书由两部分组成，第一编“因素实验设计与方差分析计算原理”中介绍了多种实验设计，其中，重复测量因素实验设计、简单效应检验、多重比较和对比等部分，都是国内尚未详细介绍过的。第二编“应用SPSS方差分析软件包处理数据”中介绍了各种实验设计的计算机数据处理程序，SPSS方差分析软件包的使用也是国内尚未系统介绍过的。
本书内容对从事心理与教育教学与研究的高等院校教师、研究生、本科生及各类研究人员开展实验研究将有很大的帮助。

计量史学研究入门，ISBN：9787301215029，作者：霍俊江 著

Beginning graduate students in mathematics and other quantitative subjects are expected to have a daunting breadth of mathematical knowledge. But few have such a background. This 2002 book will help students to see the broad outline of mathematics and to fill in the gaps in their knowledge. The author explains the basic points and a few key results of all the most important undergraduate topics in mathematics, emphasizing the intuitions behind the subject. The topics include linear algebra, vector calculus, differential geometry, real analysis, point-set topology, probability, complex analysis, abstract algebra, and more. An annotated bibliography then offers a guide to further reading and to more rigorous foundations. This book will be an essential resource for advanced undergraduate and beginning graduate students in mathematics, the physical sciences, engineering, computer science, statistics, and economics who need to quickly learn some serious mathematics.

This comprehensive reference work provides an overview of the concepts, methodologies, and applications in computational linguistics and natural language processing (NLP). Features contributions by the top researchers in the field, reflecting the work that is driving the discipline forward Includes an introduction to the major theoretical issues in these fields, as well as the central engineering applications that the work has produced Presents the major developments in an accessible way, explaining the close connection between scientific understanding of the computational properties of natural language and the creation of effective language technologies Serves as an invaluable state-of-the-art reference source for computational linguists and software engineers developing NLP applications in industrial research and development labs of software companies

Elementary set theory accustoms the students to mathematical abstraction, includes the standard constructions of relations, functions, and orderings, and leads to a discussion of the various orders of infinity. The material on logic covers not only the standard statement logic and first-order predicate logic but includes an introduction to formal systems, axiomatization, and model theory. The section on algebra is presented with an emphasis on lattices as well as Boolean and Heyting algebras. Background for recent research in natural language semantics includes sections on lambda-abstraction and generalized quantifiers. Chapters on automata theory and formal languages contain a discussion of languages between context-free and context-sensitive and form the background for much current work in syntactic theory and computational linguistics. The many exercises not only reinforce basic skills but offer an entry to linguistic applications of mathematical concepts. For upper-level undergraduate students and graduate students in theoretical linguistics, computer-science students with interests in computational linguistics, logic programming and artificial intelligence, mathematicians and logicians with interests in linguistics and the semantics of natural language

Using Praat for Linguistic Research by Will Styler is a practical guidebook and information package designed to help you use the Praat phonetics software package more effectively in Phonetic or Phonological research. Although it was originally written in the Spring/Summer of 2011 for the 2011 Linguistic Institute's Praat workshop, it's now available for anybody who's interested.

This classic text on multiple regression is noted for its nonmathematical, applied, and data-analytic approach. Readers profit from its verbal-conceptual exposition and frequent use of examples. The applied emphasis provides clear illustrations of the principles and provides worked examples of the types of applications that are possible. Researchers learn how to specify regression models that directly address their research questions. An overview of the fundamental ideas of multiple regression and a review of bivariate correlation and regression and other elementary statistical concepts provide a strong foundation for understanding the rest of the text. The third edition features an increased emphasis on graphics and the use of confidence intervals and effect size measures, and an accompanying website with data for most of the numerical examples along with the computer code for SPSS, SAS, and SYSTAT, at www.psypress.com/9780805822236 . Applied Multiple Regression serves as both a textbook for graduate students and as a reference tool for researchers in psychology, education, health sciences, communications, business, sociology, political science, anthropology, and economics. An introductory knowledge of statistics is required. Self-standing chapters minimize the need for researchers to refer to previous chapters.

Describing how people talk requires recording and analyzing phonetic data. This is true for researchers investigating the variant pronunciations of street names in Los Angeles, missionaries translating the Bible into a little-known tongue, and scholars obtaining data from a carefully controlled group in a laboratory experiment. Phonetic Data Analysis examines the procedures involved in describing the sounds of a language and illustrates the basic techniques of experimental phonetics, most of them requiring little more than a tape recorder, a video camera, and a computer.
This book enables readers to work with a speaker in a classroom setting or to go out into the field and make their own discoveries about how the sounds of a language are made. Peter Ladefoged, one of the world's leading phoneticians, introduces the experimental phonetic techniques for describing the major phonetic characteristics of any language. Throughout the book there are also comments, written in a more anecdotal fashion, on Ladefoged's own fieldwork.

由统计学家Woods和语言学家Hughes，Fletcher合著的《语言研究中的统计方法》（Statistics in language studies）是剑桥语言学系列教材之一。这本书结合语言习得、语言变异和语言测试等方面的大量研究实例，介绍了统计分析的基本概念、方法和技术。读者可以把这些技术应用到自己的研究领域中去，也可以作为一种知识基础，评价和利用统计分析文献。

This text is intended to help readers understand and critique research in language learning. The paperback edition is intended to help readers understand and critique research in language learning. It presents a balanced and objective view of a range of methods - including formal experiments, introspective methods (including diaries, logs, journals, and stimulated recall), interaction and transcript analysis, ethnography, and case studies. Other topics covered are elicitation techniques, program evaluation, and action research. The book is highly accessible and does not assume specialist or technical knowledge. This volume will be of interest to students of applied linguistics and educational researchers, in addition to classroom teachers and teachers-in-training. After reading the book and completing the tasks and exercises included in each chapter, readers should be able to understand and critique published studies in the field of language learning. They should also have acquired sufficient skills and knowledge to formulate research questions, collect relevant data relating to the questions, analyze and interpret the data, and report the results to others. Throughout the book, theoretical issues are drawn from published studies and reports. The book also emphasizes to language teachers the professional and practical value of reading published research, and initiating their own research

For the past forty years, linguistics has been dominated by the idea that language is categorical and linguistic competence discrete. It has become increasingly clear, however, that many levels of representation, from phonemes to sentence structure, show probabilistic properties, as does the language faculty. Probabilistic linguistics conceptualizes categories as distributions and views knowledge of language not as a minimal set of categorical constraints but as a set of gradient rules that may be characterized by a statistical distribution. Whereas categorical approaches focus on the endpoints of distributions of linguistic phenomena, probabilistic approaches focus on the gradient middle ground. Probabilistic linguistics integrates all the progress made by linguistics thus far with a probabilistic perspective.This book presents a comprehensive introduction to probabilistic approaches to linguistic inquiry. It covers the application of probabilistic techniques to phonology, morphology, semantics, syntax, language acquisition, psycholinguistics, historical linguistics, and sociolinguistics. It also includes a tutorial on elementary probability theory and probabilistic grammars.

This book demonstrates the contribution that statistics can and should make to linguistic studies.The range of work to which statistical analysis is applicable is vast:including,for example,language acquisition,language variation and many aspects of applied linguistics,The aubhors give a wide variety of linguixtic examples to demonstrate the use of statistics in summarising data in the most appropriate way,and then making helpful inferences form the processed information.
Students and resesarchers in many fields of linguistics will find this book an invaluable introduction to the use of statistics,and a practical text tor the development of skils in the application of statistics.