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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.
This book provides a semiotic analysis of computer programs along three axes: models of signs, kinds of signs, and systems of signs. Because computer programs are well defined and rigid, applying semiotic theories to them will help to reorganize the semiotic theories themselves. Moreover, semiotic discussion of programming theory can provide possible explanations for why programming has developed as it has and how computation is fundamentally related to human semiosis. The goal of this book is to consider the question of what computers can and cannot do, by analyzing how computer sign systems compare to those of humans. A key concept throughout is reflexivity - the capability of a system or function to reinterpret what it has produced by itself. Sign systems are reflexive by nature, and humans know how to make the most of this characteristic but have not yet fully implemented it into computer systems. Therefore, the limitations of current computers can be ascribed to insufficient reflexivity.
This book provides a semiotic analysis of computer programs along three axes: models of signs, kinds of signs, and systems of signs. Because computer programs are well defined and rigid, applying semiotic theories to them will help to reorganize the semiotic theories themselves. Moreover, semiotic discussion of programming theory can provide possible explanations for why programming has developed as it has and how computation is fundamentally related to human semiosis. The goal of this book is to consider the question of what computers can and cannot do, by analyzing how computer sign systems compare to those of humans. A key concept throughout is reflexivity - the capability of a system or function to reinterpret what it has produced by itself. Sign systems are reflexive by nature, and humans know how to make the most of this characteristic but have not yet fully implemented it into computer systems. Therefore, the limitations of current computers can be ascribed to insufficient reflexivity.
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.
Text entry has never been so important as it is today. This is in
large part due to the phenomenal, relatively recent success of
mobile computing, text messaging on mobile phones, and the
proliferation of small devices like the Blackberry and Palm Pilot.
Compared with the recent past, when text entry was primarily
through the standard "qwerty" keyboard, people today use a diverse
array of devices with the number and variety of such devices ever
increasing.
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