Statistical Methods for Annotation Analysis
Statistical Methods for Annotation Analysis

Author: Silviu Paun

Publisher: Morgan & Claypool Publishers

Published: 2022-01-13

Total Pages: 218

ISBN-13: 1636392547

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Labelling data is one of the most fundamental activities in science, and has underpinned practice, particularly in medicine, for decades, as well as research in corpus linguistics since at least the development of the Brown corpus. With the shift towards Machine Learning in Artificial Intelligence (AI), the creation of datasets to be used for training and evaluating AI systems, also known in AI as corpora, has become a central activity in the field as well. Early AI datasets were created on an ad-hoc basis to tackle specific problems. As larger and more reusable datasets were created, requiring greater investment, the need for a more systematic approach to dataset creation arose to ensure increased quality. A range of statistical methods were adopted, often but not exclusively from the medical sciences, to ensure that the labels used were not subjective, or to choose among different labels provided by the coders. A wide variety of such methods is now in regular use. This book is meant to provide a survey of the most widely used among these statistical methods supporting annotation practice. As far as the authors know, this is the first book attempting to cover the two families of methods in wider use. The first family of methods is concerned with the development of labelling schemes and, in particular, ensuring that such schemes are such that sufficient agreement can be observed among the coders. The second family includes methods developed to analyze the output of coders once the scheme has been agreed upon, particularly although not exclusively to identify the most likely label for an item among those provided by the coders. The focus of this book is primarily on Natural Language Processing, the area of AI devoted to the development of models of language interpretation and production, but many if not most of the methods discussed here are also applicable to other areas of AI, or indeed, to other areas of Data Science.

Statistical Methods for Annotation Analysis
Statistical Methods for Annotation Analysis

Author: Silviu Paun

Publisher: Springer Nature

Published: 2022-05-31

Total Pages: 208

ISBN-13: 3031037634

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Labelling data is one of the most fundamental activities in science, and has underpinned practice, particularly in medicine, for decades, as well as research in corpus linguistics since at least the development of the Brown corpus. With the shift towards Machine Learning in Artificial Intelligence (AI), the creation of datasets to be used for training and evaluating AI systems, also known in AI as corpora, has become a central activity in the field as well. Early AI datasets were created on an ad-hoc basis to tackle specific problems. As larger and more reusable datasets were created, requiring greater investment, the need for a more systematic approach to dataset creation arose to ensure increased quality. A range of statistical methods were adopted, often but not exclusively from the medical sciences, to ensure that the labels used were not subjective, or to choose among different labels provided by the coders. A wide variety of such methods is now in regular use. This book is meant to provide a survey of the most widely used among these statistical methods supporting annotation practice. As far as the authors know, this is the first book attempting to cover the two families of methods in wider use. The first family of methods is concerned with the development of labelling schemes and, in particular, ensuring that such schemes are such that sufficient agreement can be observed among the coders. The second family includes methods developed to analyze the output of coders once the scheme has been agreed upon, particularly although not exclusively to identify the most likely label for an item among those provided by the coders. The focus of this book is primarily on Natural Language Processing, the area of AI devoted to the development of models of language interpretation and production, but many if not most of the methods discussed here are also applicable to other areas of AI, or indeed, to other areas of Data Science.

Statistical Methods for Meta-Analysis
Statistical Methods for Meta-Analysis

Author: Larry V. Hedges

Publisher: Academic Press

Published: 2014-06-28

Total Pages: 392

ISBN-13: 0080570658

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The main purpose of this book is to address the statistical issues for integrating independent studies. There exist a number of papers and books that discuss the mechanics of collecting, coding, and preparing data for a meta-analysis , and we do not deal with these. Because this book concerns methodology, the content necessarily is statistical, and at times mathematical. In order to make the material accessible to a wider audience, we have not provided proofs in the text. Where proofs are given, they are placed as commentary at the end of a chapter. These can be omitted at the discretion of the reader.Throughout the book we describe computational procedures whenever required. Many computations can be completed on a hand calculator, whereas some require the use of a standard statistical package such as SAS, SPSS, or BMD. Readers with experience using a statistical package or who conduct analyses such as multiple regression or analysis of variance should be able to carry out the analyses described with the aid of a statistical package.

Statistical Methods in Language and Linguistic Research
Statistical Methods in Language and Linguistic Research

Author: Pascual Cantos Gómez

Publisher: Equinox Publishing (Indonesia)

Published: 2013-01-01

Total Pages: 260

ISBN-13: 9781845534318

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The linguistic community tend to regard statistical methods, or more generally quantitative techniques, with a certain amount of fear and suspicion. There is a feeling that statistics falls in the province of science and mathematics and such methods may destroy the magic of the literary text. This book seeks to make quantitative methods and statistical techniques less forbidding and show how they can contribute to linguistic analysis and research. It present some mathematical and statistical properties of natural languages and introduces some of the quantitative methods which are of the most value in working empirically with texts and corpora. The various issues are illustrated with helpful examples from the most basic descriptive techniques to decision-taking techniques and to more sophisticated multivariate statistical language models.

Handbook of Statistical Genomics
Handbook of Statistical Genomics

Author: David J. Balding

Publisher: John Wiley & Sons

Published: 2019-07-09

Total Pages: 1740

ISBN-13: 1119429250

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A timely update of a highly popular handbook on statistical genomics This new, two-volume edition of a classic text provides a thorough introduction to statistical genomics, a vital resource for advanced graduate students, early-career researchers and new entrants to the field. It introduces new and updated information on developments that have occurred since the 3rd edition. Widely regarded as the reference work in the field, it features new chapters focusing on statistical aspects of data generated by new sequencing technologies, including sequence-based functional assays. It expands on previous coverage of the many processes between genotype and phenotype, including gene expression and epigenetics, as well as metabolomics. It also examines population genetics and evolutionary models and inference, with new chapters on the multi-species coalescent, admixture and ancient DNA, as well as genetic association studies including causal analyses and variant interpretation. The Handbook of Statistical Genomics focuses on explaining the main ideas, analysis methods and algorithms, citing key recent and historic literature for further details and references. It also includes a glossary of terms, acronyms and abbreviations, and features extensive cross-referencing between chapters, tying the different areas together. With heavy use of up-to-date examples and references to web-based resources, this continues to be a must-have reference in a vital area of research. Provides much-needed, timely coverage of new developments in this expanding area of study Numerous, brand new chapters, for example covering bacterial genomics, microbiome and metagenomics Detailed coverage of application areas, with chapters on plant breeding, conservation and forensic genetics Extensive coverage of human genetic epidemiology, including ethical aspects Edited by one of the leading experts in the field along with rising stars as his co-editors Chapter authors are world-renowned experts in the field, and newly emerging leaders. The Handbook of Statistical Genomics is an excellent introductory text for advanced graduate students and early-career researchers involved in statistical genetics.

Metabolomics
Metabolomics

Author: Vijay Soni

Publisher: Springer Nature

Published: 2023-10-24

Total Pages: 527

ISBN-13: 3031390946

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This book Introduces the extensive applications of metabolomics from all possible areas of research and development so that not only an undergraduate can understand the advancement of metabolomics, but an entrepreneur can harness the knowledge to address possible problems to make a perfect tool to address their research question. Topics covered include the role of metabolomics in the development of agriculture, plant pathology, and their applications; the generalized application of the metabolomics and use of related technologies in various sectors of industries; and the future of metabolomics and upcoming related technologies that can fill the gap between different -omics and their applications for the betterment of humankind. This is an ideal book for university professors, researchers, and advanced-level scientists who are exploring different avenues in metabolomics. Availability of this concise information in one place will aid scientists by expanding their arsenal of techniques and can be helpful to bring more collaborations and to identify the expert at the global level.

Handbook of Statistical Genetics
Handbook of Statistical Genetics

Author: David J. Balding

Publisher: John Wiley & Sons

Published: 2008-06-10

Total Pages: 1616

ISBN-13: 9780470997628

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The Handbook for Statistical Genetics is widely regarded as the reference work in the field. However, the field has developed considerably over the past three years. In particular the modeling of genetic networks has advanced considerably via the evolution of microarray analysis. As a consequence the 3rd edition of the handbook contains a much expanded section on Network Modeling, including 5 new chapters covering metabolic networks, graphical modeling and inference and simulation of pedigrees and genealogies. Other chapters new to the 3rd edition include Human Population Genetics, Genome-wide Association Studies, Family-based Association Studies, Pharmacogenetics, Epigenetics, Ethic and Insurance. As with the second Edition, the Handbook includes a glossary of terms, acronyms and abbreviations, and features extensive cross-referencing between the chapters, tying the different areas together. With heavy use of up-to-date examples, real-life case studies and references to web-based resources, this continues to be must-have reference in a vital area of research. Edited by the leading international authorities in the field. David Balding - Department of Epidemiology & Public Health, Imperial College An advisor for our Probability & Statistics series, Professor Balding is also a previous Wiley author, having written Weight-of-Evidence for Forensic DNA Profiles, as well as having edited the two previous editions of HSG. With over 20 years teaching experience, he’s also had dozens of articles published in numerous international journals. Martin Bishop – Head of the Bioinformatics Division at the HGMP Resource Centre As well as the first two editions of HSG, Dr Bishop has edited a number of introductory books on the application of informatics to molecular biology and genetics. He is the Associate Editor of the journal Bioinformatics and Managing Editor of Briefings in Bioinformatics. Chris Cannings – Division of Genomic Medicine, University of Sheffield With over 40 years teaching in the area, Professor Cannings has published over 100 papers and is on the editorial board of many related journals. Co-editor of the two previous editions of HSG, he also authored a book on this topic.

Linguistics
Linguistics

Author: Ron Legarski

Publisher: SolveForce

Published: 2024-08-27

Total Pages: 2203

ISBN-13:

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Linguistics: The Study of Language is an insightful exploration into the world of language and its intricate structure. This book offers a comprehensive guide through the various branches of linguistics, providing readers with an in-depth understanding of how language is formed, used, and evolves over time. From the basics of phonetics and phonology to the complexities of syntax and semantics, this book covers every aspect of language study. It delves into the cognitive processes behind language acquisition, the social factors influencing language use, and the neural mechanisms that enable language processing in the brain. Each chapter is meticulously structured to guide the reader through the foundational concepts and advanced topics, making it an essential resource for both beginners and seasoned linguists. The book also touches on the practical applications of linguistics in the real world, including language teaching, translation, computational linguistics, and forensic analysis. By examining the role of language in society and the impact of technology on communication, this book equips readers with the knowledge to understand the ever-evolving nature of human language. Whether you’re a student of linguistics, a language enthusiast, or someone interested in understanding the nuances of human communication, Linguistics: The Study of Language provides a clear and engaging overview of one of humanity’s most fundamental tools.