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This textbook gives a systematized and compact summary, providing the most essential types of modern models for languages and computation together with their properties and applications. Most of these models properly reflect and formalize current computational methods, based on parallelism, distribution and cooperation covered in this book. As a result, it allows the user to develop, study, and improve these methods very effectively. This textbook also represents the first systematic treatment of modern language models for computation. It covers all essential theoretical topics concerning them. From a practical viewpoint, it describes various concepts, methods, algorithms, techniques, and software units based upon these models. Based upon them, it describes several applications in biology, linguistics, and computer science. Advanced-level students studying computer science, mathematics, linguistics and biology will find this textbook a valuable resource. Theoreticians, practitioners and researchers working in today’s theory of computation and its applications will also find this book essential as a reference.
The theory of computation is used to address challenges arising in many computer science areas such as artificial intelligence, language processors, compiler writing, information and coding systems, programming language design, computer architecture and more. To grasp topics concerning this theory readers need to familiarize themselves with its computational and language models, based on concepts of discrete mathematics including sets, relations, functions, graphs and logic.
An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives. “Written by three experts in the field, Deep Learning is the only comprehensive book on the subject.” —Elon Musk, cochair of OpenAI; cofounder and CEO of Tesla and SpaceX Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning. The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models. Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.
Jumping Computation: Updating Automata and Grammars for Discontinuous Information Processing is primarily a theoretically oriented treatment of jumping automata and grammars, covering all essential theoretical topics concerning them, including their power, properties, and transformations. From a practical viewpoint, it describes various concepts, methods, algorithms, techniques, case studies and applications based upon these automata and grammars. In today’s computerized world, the scientific development and study of computation, referred to as the theory of computation, plays a crucial role. One important branch, language theory, investigates how to define and study languages and their models, which formalize algorithms according to which their computation is executed. These language-defining models are classified into two basic categories: automata, which define languages by recognizing their words, and grammars, which generate them. Introduced many decades ago, these rules reflect classical sequential computation. However, today’s computational methods frequently process information in a fundamentally different way, frequently “jumping” over large portions of the information as a whole. This book adapts classical models to formalize and study this kind of computation properly. Simply put, during their language-defining process, these adapted versions, called jumping automata and grammars, jump across the words they work on. The book selects important models and summarizes key results about them in a compact and uniform way. It relates each model to a particular form of modern computation, such as sequential, semi-parallel and totally parallel computation, and explains how the model in question properly reflects and formalizes the corresponding form of computation, thus allowing us to obtain a systematized body of mathematically precise knowledge concerning the jumping computation. The book pays a special attention to power, closure properties, and transformations, and also describes many algorithms that modify jumping grammars and automata so they satisfy some prescribed properties without changing the defined language. The book will be of great interest to anyone researching the theory of computation across the fields of computer science, mathematics, engineering, logic and linguistics.
This three-volume set constitutes the refereed proceedings of the 12th National CCF Conference on Natural Language Processing and Chinese Computing, NLPCC 2023, held in Foshan, China, during October 12–15, 2023. The ____ regular papers included in these proceedings were carefully reviewed and selected from 478 submissions. They were organized in topical sections as follows: dialogue systems; fundamentals of NLP; information extraction and knowledge graph; machine learning for NLP; machine translation and multilinguality; multimodality and explainability; NLP applications and text mining; question answering; large language models; summarization and generation; student workshop; and evaluation workshop.
Semantic change — how the meanings of words change over time — has preoccupied scholars since well before modern linguistics emerged in the late 19th and early 20th century, ushering in a new methodological turn in the study of language change. Compared to changes in sound and grammar, semantic change is the least understood. Ever since, the study of semantic change has progressed steadily, accumulating a vast store of knowledge for over a century, encompassing many languages and language families. Historical linguists also early on realized the potential of computers as research tools, with papers at the very first international conferences in computational linguistics in the 1960s. Such computational studies still tended to be small-scale, method-oriented, and qualitative. However, recent years have witnessed a sea-change in this regard. Big-data empirical quantitative investigations are now coming to the forefront, enabled by enormous advances in storage capability and processing power. Diachronic corpora have grown beyond imagination, defying exploration by traditional manual qualitative methods, and language technology has become increasingly data-driven and semantics-oriented. These developments present a golden opportunity for the empirical study of semantic change over both long and short time spans. A major challenge presently is to integrate the hard-earned knowledge and expertise of traditional historical linguistics with cutting-edge methodology explored primarily in computational linguistics. The idea for the present volume came out of a concrete response to this challenge. The 1st International Workshop on Computational Approaches to Historical Language Change (LChange'19), at ACL 2019, brought together scholars from both fields. This volume offers a survey of this exciting new direction in the study of semantic change, a discussion of the many remaining challenges that we face in pursuing it, and considerably updated and extended versions of a selection of the contributions to the LChange'19 workshop, addressing both more theoretical problems — e.g., discovery of "laws of semantic change" — and practical applications, such as information retrieval in longitudinal text archives.
New and classical results in computational complexity, including interactive proofs, PCP, derandomization, and quantum computation. Ideal for graduate students.
In this comprehensive and cutting-edge volume, Qureshi and Jeon bring together experts from around the world to explore the potential of artificial intelligence models in research and discuss the potential benefits and the concerns and challenges that the rapid development of this field has raised. The international chapter contributor group provides a wealth of technical information on different aspects of AI, including key aspects of AI, deep learning and machine learning models for AI, natural language processing and computer vision, reinforcement learning, ethics and responsibilities, security, practical implementation, and future directions. The contents are balanced in terms of theory, methodologies, and technical aspects, and contributors provide case studies to clearly illustrate the concepts and technical discussions throughout. Readers will gain valuable insights into how AI can revolutionize their work in fields including data analytics and pattern identification, healthcare research, social science research, and more, and improve their technical skills, problem-solving abilities, and evidence-based decision-making. Additionally, they will be cognizant of the limitations and challenges, the ethical implications, and security concerns related to language models, which will enable them to make more informed choices regarding their implementation. This book is an invaluable resource for undergraduate and graduate students who want to understand AI models, recent trends in the area, and technical and ethical aspects of AI. Companies involved in AI development or implementing AI in various fields will also benefit from the book’s discussions on both the technical and ethical aspects of this rapidly growing field.