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A mechanistic theory of the representation and use of semantic knowledge that uses distributed connectionist networks as a starting point for a psychological theory of semantic cognition.
For readers looking to understand lexical access and word-finding difficulty (WFD), Semantic Processing and Word Finding Difficulty Across the Lifespan: A Practical Guide for Speech-Language Pathologists provides a comprehensive review of current research and clinical approaches to establish a holistic, interdisciplinary understanding of lexical access and retrieval difficulty across different communication disorders. By including practical guidelines and protocols, this professional text can help speech-language pathologists (SLPs) and other related professionals bridge the gap between research and clinical practice. This text covers a wide range of communication disorders, including developmental language disorder, autism spectrum disorder, aphasia, normal aging, and dementia. It illustrates the connections between the research evidence and clinical practice and addresses lexical learning and retrieval difficulty through a holistic lens and cognitive-linguistic frameworks. This text integrates research evidence from a variety of disciplines, including speech-language pathology, linguistics, neuroscience, and psychology. The authors take readers for a deep dive into different underlying problems that lead to lexical access and retrieval difficulty and strategies to remediate them effectively. By addressing lexical issues from a broader view, this unique resource helps readers see the connections from different perspectives to further understand the complex issues involved in lexical learning and retrieval. Key Features: * A discussion of lexical learning and expansion from birth to school-age by incorporating metalinguistic skills and considering the relationships between language domains. * An exploration of contributing factors to lexical learning and word retrieval. * A holistic review of standardized and nonstandard measures for the breadth and depth of lexical access and retrieval across the lifespan and for people with diverse cultural and linguistic backgrounds. * A comprehensive review of current available evidence-based and semantic-focused interventions for both developmental and neurogenic communication disorders. * Chapter summaries and discussion questions close each chapter. * Clinical implication sections help connect research to clinical practice. * Therapy plan examples for commonly implemented lexical intervention approaches. Disclaimer: Please note that ancillary content (such as documents, audio, and video, etc.) may not be included as published in the original print version of this book.
Spoken language understanding (SLU) is an emerging field in between speech and language processing, investigating human/ machine and human/ human communication by leveraging technologies from signal processing, pattern recognition, machine learning and artificial intelligence. SLU systems are designed to extract the meaning from speech utterances and its applications are vast, from voice search in mobile devices to meeting summarization, attracting interest from both commercial and academic sectors. Both human/machine and human/human communications can benefit from the application of SLU, using differing tasks and approaches to better understand and utilize such communications. This book covers the state-of-the-art approaches for the most popular SLU tasks with chapters written by well-known researchers in the respective fields. Key features include: Presents a fully integrated view of the two distinct disciplines of speech processing and language processing for SLU tasks. Defines what is possible today for SLU as an enabling technology for enterprise (e.g., customer care centers or company meetings), and consumer (e.g., entertainment, mobile, car, robot, or smart environments) applications and outlines the key research areas. Provides a unique source of distilled information on methods for computer modeling of semantic information in human/machine and human/human conversations. This book can be successfully used for graduate courses in electronics engineering, computer science or computational linguistics. Moreover, technologists interested in processing spoken communications will find it a useful source of collated information of the topic drawn from the two distinct disciplines of speech processing and language processing under the new area of SLU.
A new edition of the widely used guide to the key ideas, languages, and technologies of the Semantic Web The development of the Semantic Web, with machine-readable content, has the potential to revolutionize the World Wide Web and its uses. A Semantic Web Primer provides an introduction and guide to this continuously evolving field, describing its key ideas, languages, and technologies. Suitable for use as a textbook or for independent study by professionals, it concentrates on undergraduate-level fundamental concepts and techniques that will enable readers to proceed with building applications on their own and includes exercises, project descriptions, and annotated references to relevant online materials. The third edition of this widely used text has been thoroughly updated, with significant new material that reflects a rapidly developing field. Treatment of the different languages (OWL2, rules) expands the coverage of RDF and OWL, defining the data model independently of XML and including coverage of N3/Turtle and RDFa. A chapter is devoted to OWL2, the new W3C standard. This edition also features additional coverage of the query language SPARQL, the rule language RIF and the possibility of interaction between rules and ontology languages and applications. The chapter on Semantic Web applications reflects the rapid developments of the past few years. A new chapter offers ideas for term projects. Additional material, including updates on the technological trends and research directions, can be found at http://www.semanticwebprimer.org.
The seven-volume set of LNCS 11301-11307, constitutes the proceedings of the 25th International Conference on Neural Information Processing, ICONIP 2018, held in Siem Reap, Cambodia, in December 2018. The 401 full papers presented were carefully reviewed and selected from 575 submissions. The papers address the emerging topics of theoretical research, empirical studies, and applications of neural information processing techniques across different domains. The first volume, LNCS 11301, is organized in topical sections on deep neural networks, convolutional neural networks, recurrent neural networks, and spiking neural networks.
The six-volume set LNCS 14447 until 14452 constitutes the refereed proceedings of the 30th International Conference on Neural Information Processing, ICONIP 2023, held in Changsha, China, in November 2023. The 652 papers presented in the proceedings set were carefully reviewed and selected from 1274 submissions. They focus on theory and algorithms, cognitive neurosciences; human centred computing; applications in neuroscience, neural networks, deep learning, and related fields.
The two-volume set CCIS 1516 and 1517 constitutes thoroughly refereed short papers presented at the 28th International Conference on Neural Information Processing, ICONIP 2021, held in Sanur, Bali, Indonesia, in December 2021.* The volume also presents papers from the workshop on Artificial Intelligence and Cyber Security, held during the ICONIP 2021. The 176 short and workshop papers presented in this volume were carefully reviewed and selected for publication out of 1093 submissions. The papers are organized in topical sections as follows: theory and algorithms; AI and cybersecurity; cognitive neurosciences; human centred computing; advances in deep and shallow machine learning algorithms for biomedical data and imaging; reliable, robust, and secure machine learning algorithms; theory and applications of natural computing paradigms; applications. * The conference was held virtually due to the COVID-19 pandemic.
Latent semantic mapping (LSM) is a generalization of latent semantic analysis (LSA), a paradigm originally developed to capture hidden word patterns in a text document corpus. In information retrieval, LSA enables retrieval on the basis of conceptual content, instead of merely matching words between queries and documents. It operates under the assumption that there is some latent semantic structure in the data, which is partially obscured by the randomness of word choice with respect to retrieval. Algebraic and/or statistical techniques are brought to bear to estimate this structure and get rid of the obscuring ""noise."" This results in a parsimonious continuous parameter description of words and documents, which then replaces the original parameterization in indexing and retrieval. This approach exhibits three main characteristics: -Discrete entities (words and documents) are mapped onto a continuous vector space; -This mapping is determined by global correlation patterns; and -Dimensionality reduction is an integral part of the process. Such fairly generic properties are advantageous in a variety of different contexts, which motivates a broader interpretation of the underlying paradigm. The outcome (LSM) is a data-driven framework for modeling meaningful global relationships implicit in large volumes of (not necessarily textual) data. This monograph gives a general overview of the framework, and underscores the multifaceted benefits it can bring to a number of problems in natural language understanding and spoken language processing. It concludes with a discussion of the inherent tradeoffs associated with the approach, and some perspectives on its general applicability to data-driven information extraction. Contents: I. Principles / Introduction / Latent Semantic Mapping / LSM Feature Space / Computational Effort / Probabilistic Extensions / II. Applications / Junk E-mail Filtering / Semantic Classification / Language Modeling / Pronunciation Modeling / Speaker Verification / TTS Unit Selection / III. Perspectives / Discussion / Conclusion / Bibliography
A comprehensive theory-based approach to the treatment of text meaning in natural language processing applications.