Computer-Assisted Text Analysis

This is an engaging introduction to the study of language for undergraduate or beginning graduate students, aimed especially at those who would like to continue further linguistic study. It introduces students to analytical thinking about language, but goes beyond existing texts to show what it means to think like a scientist about language, through the exploration of data and interactive problem sets. A key feature of this text is its flexibility. With its focus on foundational areas of linguistics and scientific analysis, it can be used in a variety of course types, with instructors using it alongside other information or texts as appropriate for their own courses of study. The text can also serve as a supplementary text in other related fields (Speech and Hearing Sciences, Psychology, Education, Computer Science, Anthropology, and others) to help learners in these areas better understand how linguists think about and work with language data. No prerequisites are necessary. While ea

This is an engaging introduction to the study of language for undergraduate or beginning graduate students, aimed especially at those who would like to continue further linguistic study. It introduces students to analytical thinking about language, but goes beyond existing texts to show what it means to think like a scientist about language, through the exploration of data and interactive problem sets. A key feature of this text is its flexibility. With its focus on foundational areas of linguistics and scientific analysis, it can be used in a variety of course types, with instructors using it alongside other information or texts as appropriate for their own courses of study. The text can also serve as a supplementary text in other related fields (Speech and Hearing Sciences, Psychology, Education, Computer Science, Anthropology, and others) to help learners in these areas better understand how linguists think about and work with language data. No prerequisites are necessary. While ea

This textbook bypasses the need for advanced mathematics by providing in-text computer code, allowing students to explore Bayesian data analysis without the calculus background normally considered a prerequisite for this material. Now, students can use the best methods without needing advanced mathematical techniques. This approach goes beyond “frequentist” concepts of p-values and null hypothesis testing, using the full power of modern probability theory to solve real-world problems. The book offers a fully self-contained course, which demonstrates analysis techniques throughout with worked examples crafted specifically for students in the behavioral and neural sciences. The book presents two general algorithms that help students solve the measurement and model selection (also called “hypothesis testing”) problems most frequently encountered in real-world applications.

This textbook bypasses the need for advanced mathematics by providing in-text computer code, allowing students to explore Bayesian data analysis without the calculus background normally considered a prerequisite for this material. Now, students can use the best methods without needing advanced mathematical techniques. This approach goes beyond “frequentist” concepts of p-values and null hypothesis testing, using the full power of modern probability theory to solve real-world problems. The book offers a fully self-contained course, which demonstrates analysis techniques throughout with worked examples crafted specifically for students in the behavioral and neural sciences. The book presents two general algorithms that help students solve the measurement and model selection (also called “hypothesis testing”) problems most frequently encountered in real-world applications.

Selecting good-quality sperm for use in in-vitro fertilization is a key step in assisted reproduction. For many years purely morphological attributes have been used to assess suitability, but increasingly biochemical and molecular biological techniques are now identifying sperm with the best chances of producing viable and healthy embryos. Focusing on modern sperm function testing, this manual provides technical details of commonly used tests and gives an overview of the laboratory techniques used to evaluate sperm samples. Covering a variety of testing methods in detail, from manual and computer-assisted semen analysis to zona pellucida binding assays, and tests assessing sperm DNA damage such as the TUNEL assay. Describing the underlying science, practical advice for performing the tests is given, including tips for optimizing outcomes and trouble-shooting. This is an essential guide for reproductive medicine specialists, clinical andrologists, urologists and gynecologists working wi

This book is an advanced introduction to semantics that presents this crucial component of human language through the lens of the 'Meaning-Text' theory - an approach that treats linguistic knowledge as a huge inventory of correspondences between thought and speech. Formally, semantics is viewed as an organized set of rules that connect a representation of meaning (Semantic Representation) to a representation of the sentence (Deep-Syntactic Representation). The approach is particularly interesting for computer assisted language learning, natural language processing and computational lexicography, as our linguistic rules easily lend themselves to formalization and computer applications. The model combines abstract theoretical constructions with numerous linguistic descriptions, as well as multiple practice exercises that provide a solid hands-on approach to learning how to describe natural language semantics.

This book is an advanced introduction to semantics that presents this crucial component of human language through the lens of the 'Meaning-Text' theory - an approach that treats linguistic knowledge as a huge inventory of correspondences between thought and speech. Formally, semantics is viewed as an organized set of rules that connect a representation of meaning (Semantic Representation) to a representation of the sentence (Deep-Syntactic Representation). The approach is particularly interesting for computer assisted language learning, natural language processing and computational lexicography, as our linguistic rules easily lend themselves to formalization and computer applications. The model combines abstract theoretical constructions with numerous linguistic descriptions, as well as multiple practice exercises that provide a solid hands-on approach to learning how to describe natural language semantics.