Download or read book The Architecture of Cognition written by Paco Calvo and published by MIT Press. This book was released on 2014-04-18 with total page 483 pages. Available in PDF, EPUB and Kindle. Book excerpt: Philosophers and cognitive scientists reassess systematicity in the post-connectionist era, offering perspectives from ecological psychology, embodied and distributed cognition, enactivism, and other methodologies. In 1988, Jerry Fodor and Zenon Pylyshyn challenged connectionist theorists to explain the systematicity of cognition. In a highly influential critical analysis of connectionism, they argued that connectionist explanations, at best, can only inform us about details of the neural substrate; explanations at the cognitive level must be classical insofar as adult human cognition is essentially systematic. More than twenty-five years later, however, conflicting explanations of cognition do not divide along classicist-connectionist lines, but oppose cognitivism (both classicist and connectionist) with a range of other methodologies, including distributed and embodied cognition, ecological psychology, enactivism, adaptive behavior, and biologically based neural network theory. This volume reassesses Fodor and Pylyshyn's “systematicity challenge” for a post-connectionist era. The contributors consider such questions as how post-connectionist approaches meet Fodor and Pylyshyn's conceptual challenges; whether there is empirical evidence for or against the systematicity of thought; and how the systematicity of human thought relates to behavior. The chapters offer a representative sample and an overview of the most important recent developments in the systematicity debate. Contributors Ken Aizawa, William Bechtel, Gideon Borensztajn, Paco Calvo, Anthony Chemero, Jonathan D. Cohen, Alicia Coram, Jeffrey L. Elman, Stefan L. Frank, Antoni Gomila, Seth A. Herd, Trent Kriete, Christian J. Lebiere, Lorena Lobo, Edouard Machery, Gary Marcus, Emma Martín, Fernando Martínez-Manrique, Brian P. McLaughlin, Randall C. O'Reilly, Alex A. Petrov, Steven Phillips, William Ramsey, Michael Silberstein, John Symons, David Travieso, William H. Wilson, Willem Zuidema

Download or read book Supervised Machine Learning for Text Analysis in R written by Emil Hvitfeldt and published by CRC Press. This book was released on 2021-10-22 with total page 402 pages. Available in PDF, EPUB and Kindle. Book excerpt: Text data is important for many domains, from healthcare to marketing to the digital humanities, but specialized approaches are necessary to create features for machine learning from language. Supervised Machine Learning for Text Analysis in R explains how to preprocess text data for modeling, train models, and evaluate model performance using tools from the tidyverse and tidymodels ecosystem. Models like these can be used to make predictions for new observations, to understand what natural language features or characteristics contribute to differences in the output, and more. If you are already familiar with the basics of predictive modeling, use the comprehensive, detailed examples in this book to extend your skills to the domain of natural language processing. This book provides practical guidance and directly applicable knowledge for data scientists and analysts who want to integrate unstructured text data into their modeling pipelines. Learn how to use text data for both regression and classification tasks, and how to apply more straightforward algorithms like regularized regression or support vector machines as well as deep learning approaches. Natural language must be dramatically transformed to be ready for computation, so we explore typical text preprocessing and feature engineering steps like tokenization and word embeddings from the ground up. These steps influence model results in ways we can measure, both in terms of model metrics and other tangible consequences such as how fair or appropriate model results are.

Download or read book Emergentist Approaches to Language written by Brian MacWhinney and published by Frontiers Media SA. This book was released on 2022-02-16 with total page 291 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Neural Machine Translation written by Philipp Koehn and published by Cambridge University Press. This book was released on 2020-06-18 with total page 409 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learn how to build machine translation systems with deep learning from the ground up, from basic concepts to cutting-edge research.

Download or read book The Compositionality Papers written by Jerry A. Fodor and published by Oxford University Press. This book was released on 2002 with total page 228 pages. Available in PDF, EPUB and Kindle. Book excerpt: Jerry Fodor and Ernie Lepore have produced a series of original and controversial essays on issues relating to compositionality in language and mind; they have now revised them all for publication together in this volume. Compositionality is the following aspect of a system of representation:the complex symbols in the system inherit their syntactic and semantic properties from the primitive symbols of the system. Fodor and Lepore argue that compositionality determines what view we must take of the nature of concepts. Anyone trying to figure out how language and mind work must takeaccount of this challenging work by two leading figures in the field.

Download or read book The Wug Test written by Jean Berko Gleason and published by . This book was released on 2019-12-09 with total page 64 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Wug Test is a picture book for children and adults that uses invented nouns, verbs, and adjectives to illuminate what children know about their own language. This book includes the original delightful Wug Test drawings and test questions created by Professor Jean Berko Gleason in 1958. The Wug Test, first given in research settings, showed that children do not learn language simply by memorizing what they hear. Instead, they learn the rules of their language so that they are able to make plurals, past tenses and other forms when presented with words they have never heard before. This book has pictures and interesting questions to share with children, along with informative notes and commentary for adults. It provides a fascinating insight into what even very young children know about language, as well as a way to understand and observe a child's acquisition of the rules of language over time. Ages 3-7.

Download or read book Developmental Modal and Pathological Variation Linguistic and Cognitive Profiles for Speakers of Linguistically Proximal Languages and Varieties written by Kleanthes K. Grohmann and published by Frontiers Media SA. This book was released on 2018-11-08 with total page 179 pages. Available in PDF, EPUB and Kindle. Book excerpt: One significant area of research in the multifaceted field of bilingualism over the past two decades has been the demonstration, validation, and account of the so-called ‘bilingual advantage’. This refers to the hypothesis that bilingual speakers have advanced abilities in executive functions and other domains of human cognition. Such cognitive benefits of bilingualism have an impact on the processing mechanisms active during language acquisition in a way that results in language variation. Within bilingual populations, the notion of language proximity (or linguistic distance) is also of key importance for deriving variation. In addition, sociolinguistic factors can invest the process of language development and its outcome with an additional layer of complexity, such as schooling, language, dominance, competing motivations, or the emergence of mesolectal varieties, which blur the boundaries of grammatical variants. This is particularly relevant for diglossic speech communities—bilectal, bidialectal, or bivarietal speakers. The defined goal of the present Research Topic is to address whether the bilingual advantage extends to such speakers as well. Thus, ‘Linguistic and Cognitive Profiles for Speakers of Linguistically Proximal Languages and Varieties’ become an important matter within ‘Developmental, Modal, and Pathological Variation’.

Download or read book AI Powered Productivity written by Dr. Asma Asfour and published by Asma Asfour. This book was released on 2024-07-29 with total page 195 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book, "AI-Powered Productivity," aims to provide a guide to understanding, utilizing AI and generative tools in various professional settings. The primary purpose of this book is to offer readers a deep dive into the concepts, tools, and practices that define the current AI landscape. From foundational principles to advanced applications, this book is structured to cater to both beginners and professionals looking to enhance their knowledge and skills in AI. This book is divided into nine chapters, each focusing on a specific aspect of AI and its practical applications: Chapter 1 introduces the basic concepts of AI, its impact on various sectors, and key factors driving its rapid advancement, along with an overview of generative AI tools. Chapter 2 delves into large language models like ChatGPT, Google Gemini, Claude, Microsoft's Turing NLG, and Facebook's BlenderBot, exploring their integration with multimodal technologies and their effects on professional productivity. Chapter 3 offers a practical guide to mastering LLM prompting and customization, including tutorials on crafting effective prompts and advanced techniques, as well as real-world examples of AI applications. Chapter 4 examines how AI can enhance individual productivity, focusing on professional and personal benefits, ethical use, and future trends. Chapter 5 addresses data-driven decision- making, covering data analysis techniques, AI in trend identification, consumer behavior analysis, strategic planning, and product development. Chapter 6 discusses strategic and ethical considerations of AI, including AI feasibility, tool selection, multimodal workflows, and best practices for ethical AI development and deployment. Chapter 7 highlights the role of AI in transforming training and professional development, covering structured training programs, continuous learning initiatives, and fostering a culture of innovation and experimentation. Chapter 8 provides a guide to successfully implementing AI in organizations, discussing team composition, collaborative approaches, iterative development processes, and strategic alignment for AI initiatives. Finally, Chapter 9 looks ahead to the future of work, preparing readers for the AI revolution by addressing training and education, career paths, common fears, and future trends in the workforce. The primary audience for the book is professionals seeking to enhance productivity and organizations or businesses. For professionals, the book targets individuals from various industries, reflecting its aim to reach a broad audience across different professional fields. It is designed for employees at all levels, offering valuable insights to both newcomers to AI and seasoned professionals. Covering a range of topics from foundational concepts to advanced applications, the book is particularly relevant for those interested in improving efficiency, with a strong emphasis on practical applications and productivity tools to optimize work processes. For organizations and businesses, the book serves as a valuable resource for decision-makers and managers, especially with chapters on data-driven decision-making, strategic considerations, and AI implementation. HR and training professionals will find the focus on AI in training and development beneficial for talent management, while IT and technology teams will appreciate the information on AI tools and concepts.

Download or read book Deep Learning written by John D. Kelleher and published by MIT Press. This book was released on 2019-09-10 with total page 298 pages. Available in PDF, EPUB and Kindle. Book excerpt: An accessible introduction to the artificial intelligence technology that enables computer vision, speech recognition, machine translation, and driverless cars. Deep learning is an artificial intelligence technology that enables computer vision, speech recognition in mobile phones, machine translation, AI games, driverless cars, and other applications. When we use consumer products from Google, Microsoft, Facebook, Apple, or Baidu, we are often interacting with a deep learning system. In this volume in the MIT Press Essential Knowledge series, computer scientist John Kelleher offers an accessible and concise but comprehensive introduction to the fundamental technology at the heart of the artificial intelligence revolution. Kelleher explains that deep learning enables data-driven decisions by identifying and extracting patterns from large datasets; its ability to learn from complex data makes deep learning ideally suited to take advantage of the rapid growth in big data and computational power. Kelleher also explains some of the basic concepts in deep learning, presents a history of advances in the field, and discusses the current state of the art. He describes the most important deep learning architectures, including autoencoders, recurrent neural networks, and long short-term networks, as well as such recent developments as Generative Adversarial Networks and capsule networks. He also provides a comprehensive (and comprehensible) introduction to the two fundamental algorithms in deep learning: gradient descent and backpropagation. Finally, Kelleher considers the future of deep learning—major trends, possible developments, and significant challenges.

Download or read book Phonology and Language Use written by Joan Bybee and published by Cambridge University Press. This book was released on 2003-02-27 with total page 262 pages. Available in PDF, EPUB and Kindle. Book excerpt: A research perspective that takes language use into account opens up new views of old issues and provides an understanding of issues that linguists have rarely addressed. Referencing new developments in cognitive and functional linguistics, phonetics, and connectionist modeling, this book investigates various ways in which a speaker/hearer's experience with language affects the representation of phonology. Rather than assuming phonological representations in terms of phonemes, Joan Bybee adopts an exemplar model, in which specific tokens of use are stored and categorized phonetically with reference to variables in the context. This model allows an account of phonetically gradual sound change which produces lexical variation, and provides an explanatory account of the fact that many reductive sound changes affect high frequency items first. The well-known effects of type and token frequency on morphologically-conditioned phonological alterations are shown also to apply to larger sequences, such as fixed phrases and constructions, solving some of the problems formulated previously as dealing with the phonology-syntax interface.

Download or read book EVALITA Proceedings of the Eighth Evaluation Campaign of Natural Language Processing and Speech Tools for Italian Final Workshop written by AA.VV. and published by Accademia University Press. This book was released on 2024-01-17 with total page 362 pages. Available in PDF, EPUB and Kindle. Book excerpt: EVALITA 2023 is an initiative of AILC (Associazione Italiana di Linguistica Computazionale) and it is endorsed by the Italian Association for Artificial Intelligence (AIxIA) and the Italian Association for Speech Sciences (AISV). As in the previous editions, EVALITA 2023 is organized along a set of selected tasks, which provide participants with opportunities to discuss and explore both emerging and traditional areas of Natural Language Processing and Speech for Italian. The participation is encouraged for teams working both in academic institutions and industrial organizations.

Download or read book Learning Deep Learning written by Magnus Ekman and published by Addison-Wesley Professional. This book was released on 2021-07-19 with total page 1106 pages. Available in PDF, EPUB and Kindle. Book excerpt: NVIDIA's Full-Color Guide to Deep Learning: All You Need to Get Started and Get Results "To enable everyone to be part of this historic revolution requires the democratization of AI knowledge and resources. This book is timely and relevant towards accomplishing these lofty goals." -- From the foreword by Dr. Anima Anandkumar, Bren Professor, Caltech, and Director of ML Research, NVIDIA "Ekman uses a learning technique that in our experience has proven pivotal to success—asking the reader to think about using DL techniques in practice. His straightforward approach is refreshing, and he permits the reader to dream, just a bit, about where DL may yet take us." -- From the foreword by Dr. Craig Clawson, Director, NVIDIA Deep Learning Institute Deep learning (DL) is a key component of today's exciting advances in machine learning and artificial intelligence. Learning Deep Learning is a complete guide to DL. Illuminating both the core concepts and the hands-on programming techniques needed to succeed, this book is ideal for developers, data scientists, analysts, and others--including those with no prior machine learning or statistics experience. After introducing the essential building blocks of deep neural networks, such as artificial neurons and fully connected, convolutional, and recurrent layers, Magnus Ekman shows how to use them to build advanced architectures, including the Transformer. He describes how these concepts are used to build modern networks for computer vision and natural language processing (NLP), including Mask R-CNN, GPT, and BERT. And he explains how a natural language translator and a system generating natural language descriptions of images. Throughout, Ekman provides concise, well-annotated code examples using TensorFlow with Keras. Corresponding PyTorch examples are provided online, and the book thereby covers the two dominating Python libraries for DL used in industry and academia. He concludes with an introduction to neural architecture search (NAS), exploring important ethical issues and providing resources for further learning. Explore and master core concepts: perceptrons, gradient-based learning, sigmoid neurons, and back propagation See how DL frameworks make it easier to develop more complicated and useful neural networks Discover how convolutional neural networks (CNNs) revolutionize image classification and analysis Apply recurrent neural networks (RNNs) and long short-term memory (LSTM) to text and other variable-length sequences Master NLP with sequence-to-sequence networks and the Transformer architecture Build applications for natural language translation and image captioning NVIDIA's invention of the GPU sparked the PC gaming market. The company's pioneering work in accelerated computing--a supercharged form of computing at the intersection of computer graphics, high-performance computing, and AI--is reshaping trillion-dollar industries, such as transportation, healthcare, and manufacturing, and fueling the growth of many others. Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.

Download or read book Morphological Productivity written by Laurie Bauer and published by Cambridge University Press. This book was released on 2001-05-28 with total page 261 pages. Available in PDF, EPUB and Kindle. Book excerpt: Why are there more English words ending in -ness than ending in -ity? What is it about some endings that makes them more widely usable than others? Can we measure the differences in the facility with which the various affixes are used? Does the difference in facility reflect a difference in the way we treat words containing these affixes in the brain? These are the questions examined in this book. Morphological productivity has, over the centuries, been a major factor in providing the huge vocabulary of English and remains one of the most contested areas in the study of word-formation and structure. This book takes an eclectic approach to the topic, applying the findings for morphology to syntax and phonology. Bringing together the results of twenty years' work in the field, it provides new insights and considers a wide range of linguistic and psycholinguistic evidence.

Download or read book Representation Learning for Natural Language Processing written by Zhiyuan Liu and published by Springer Nature. This book was released on 2020-07-03 with total page 319 pages. Available in PDF, EPUB and Kindle. Book excerpt: This open access book provides an overview of the recent advances in representation learning theory, algorithms and applications for natural language processing (NLP). It is divided into three parts. Part I presents the representation learning techniques for multiple language entries, including words, phrases, sentences and documents. Part II then introduces the representation techniques for those objects that are closely related to NLP, including entity-based world knowledge, sememe-based linguistic knowledge, networks, and cross-modal entries. Lastly, Part III provides open resource tools for representation learning techniques, and discusses the remaining challenges and future research directions. The theories and algorithms of representation learning presented can also benefit other related domains such as machine learning, social network analysis, semantic Web, information retrieval, data mining and computational biology. This book is intended for advanced undergraduate and graduate students, post-doctoral fellows, researchers, lecturers, and industrial engineers, as well as anyone interested in representation learning and natural language processing.

Download or read book Supervised Sequence Labelling with Recurrent Neural Networks written by Alex Graves and published by Springer. This book was released on 2012-02-06 with total page 148 pages. Available in PDF, EPUB and Kindle. Book excerpt: Supervised sequence labelling is a vital area of machine learning, encompassing tasks such as speech, handwriting and gesture recognition, protein secondary structure prediction and part-of-speech tagging. Recurrent neural networks are powerful sequence learning tools—robust to input noise and distortion, able to exploit long-range contextual information—that would seem ideally suited to such problems. However their role in large-scale sequence labelling systems has so far been auxiliary. The goal of this book is a complete framework for classifying and transcribing sequential data with recurrent neural networks only. Three main innovations are introduced in order to realise this goal. Firstly, the connectionist temporal classification output layer allows the framework to be trained with unsegmented target sequences, such as phoneme-level speech transcriptions; this is in contrast to previous connectionist approaches, which were dependent on error-prone prior segmentation. Secondly, multidimensional recurrent neural networks extend the framework in a natural way to data with more than one spatio-temporal dimension, such as images and videos. Thirdly, the use of hierarchical subsampling makes it feasible to apply the framework to very large or high resolution sequences, such as raw audio or video. Experimental validation is provided by state-of-the-art results in speech and handwriting recognition.

Download or read book What is Morphology written by Mark Aronoff and published by John Wiley & Sons. This book was released on 2022-11-21 with total page 342 pages. Available in PDF, EPUB and Kindle. Book excerpt: What is Morphology? PRAISE FOR THIS EDITION “What is Morphology? continues to be the most attractive, accessible, and entertaining introduction to morphology on the market. It is a lively conversation between the authors and their readers, which radiates enthusiasm for its subject, while stimulating readers to think about morphology for themselves through numerous skillfully designed examples and exercises. The book is deliberately theory-neutral but leaves no doubt about the fundamental importance of morphology within linguistic theory.” Martin Maiden, University of Oxford “It’s no surprise that What is Morphology? is now appearing in a third edition. Aronoff and Fudeman’s textbook is a fun, highly accessible introduction to morphology, providing an excellent grounding in the key distinctions and theoretical issues.” Dunstan Brown, University of York PRAISE FOR PRIOR EDITION “Aronoff and Fudeman have produced a clear and jargon-free introduction to contemporary morphological theory and practice. The book succeeds particularly in clarifying the empirical content, organizational principles and analytic techniques that distinguish morphology from other fields of linguistics.” James P. Blevins, University of Cambridge What is Morphology? Third Edition, provides a critical introduction to the central ideas and perennial issues of linguistic morphology. Assuming only minimal background in linguistics, this student-friendly textbook uses clear language and easy-to-understand examples to describe fundamental morphological phenomena and their interactions with phonology, syntax, and semantics. The chapters of this textbook equip students with the skills needed to analyze a range of classic morphological problems through engaging examples and student-friendly explanations. Cross-linguistic data from Kujamaat Jóola, a West African language, is incorporated throughout the text to explain and clarify morphemes, morphophonology, inflection, syncretism, lexemes, and other essential concepts. Fully revised and updated, the third edition also contains an entirely new chapter on computational linguistics, and a wealth of additional exercises, examples, and further readings. What is Morphology? Third Edition, is the perfect textbook for undergraduate and graduate students studying morphology for the first time, a useful reference for researchers and scholars with limited experience in linguistic morphology, and a valuable resource for professionals working in areas such as speech recognition, natural language understanding, machine translation, and natural language generation. An instructor website is available at www.wiley.com/go/aronoff/morphology3e.

Download or read book Handbook of Natural Language Processing written by Robert Dale and published by CRC Press. This book was released on 2000-07-25 with total page 974 pages. Available in PDF, EPUB and Kindle. Book excerpt: This study explores the design and application of natural language text-based processing systems, based on generative linguistics, empirical copus analysis, and artificial neural networks. It emphasizes the practical tools to accommodate the selected system.