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This book presents the complete collection of peer-reviewed presentations at the 1999 Cognitive Science Society meeting, including papers, poster abstracts, and descriptions of conference symposia. For students and researchers in all areas of cognitive science.
This book gathers high-quality papers presented at the 2nd International Conference on Communication, Devices & Computing (ICCDC 2019), held at Haldia Institute of Technology from March 14–15, 2019. The papers are divided into three main areas: communication technologies, electronics circuits & devices and computing. Written by students and researchers from around the world, they accurately reflect the global status quo.
In vielen Bereichen der Linguistik werden Textkorpora, Sprachkorpora oder multimodale Korpora heute als empirische Basis verwendet. Aufbauend auf Methoden des 19. Jahrhunderts haben sich dabei mit dem Aufkommen von elektronischen Korpora seit den 1940ern neue Standards für linguistische Annotation und Vorverarbeitung sowie für qualitative und quantitative Untersuchungen entwickelt. Das Handbuch bietet einen umfassenden Überblick über Geschichte, Methoden und Anwendungen der Korpuslinguistik. Die einzelnen Überblicks- und Spezialartikel sind von Experten und Expertinnen der jeweiligen Gebiete geschrieben. Dabei wird auf klare und umfassende Darstellung, eine gute Vernetzung zwischen den Artikel und weiterführende Hinweise Wert gelegt.
This book gathers the proceedings of the 3rd International Conference on Advanced Intelligent Systems and Informatics 2017 (AISI2017), which took place in Cairo, Egypt from September 9 to 11, 2017. This international and interdisciplinary conference, which highlighted essential research and developments in the field of informatics and intelligent systems, was organized by the Scientific Research Group in Egypt (SRGE). The book’s content is divided into five main sections: Intelligent Language Processing, Intelligent Systems, Intelligent Robotics Systems, Informatics, and the Internet of Things.
Ruslan Mitkov's highly successful Oxford Handbook of Computational Linguistics has been substantially revised and expanded in this second edition. Alongside updated accounts of the topics covered in the first edition, it includes 17 new chapters on subjects such as semantic role-labelling, text-to-speech synthesis, translation technology, opinion mining and sentiment analysis, and the application of Natural Language Processing in educational and biomedical contexts, among many others. The volume is divided into four parts that examine, respectively: the linguistic fundamentals of computational linguistics; the methods and resources used, such as statistical modelling, machine learning, and corpus annotation; key language processing tasks including text segmentation, anaphora resolution, and speech recognition; and the major applications of Natural Language Processing, from machine translation to author profiling. The book will be an essential reference for researchers and students in computational linguistics and Natural Language Processing, as well as those working in related industries.
This two-volume set, consisting of LNCS 7181 and LNCS 7182, constitutes the thoroughly refereed proceedings of the 13th International Conference on Computer Linguistics and Intelligent Processing, held in New Delhi, India, in March 2012. The total of 92 full papers were carefully reviewed and selected for inclusion in the proceedings. The contents have been ordered according to the following topical sections: NLP system architecture; lexical resources; morphology and syntax; word sense disambiguation and named entity recognition; semantics and discourse; sentiment analysis, opinion mining, and emotions; natural language generation; machine translation and multilingualism; text categorization and clustering; information extraction and text mining; information retrieval and question answering; document summarization; and applications.
This book comprises a set of articles that specify the methodology of text mining, describe the creation of lexical resources in the framework of text mining and use text mining for various tasks in natural language processing (NLP). The analysis of large amounts of textual data is a prerequisite to build lexical resources such as dictionaries and ontologies and also has direct applications in automated text processing in fields such as history, healthcare and mobile applications, just to name a few. This volume gives an update in terms of the recent gains in text mining methods and reflects the most recent achievements with respect to the automatic build-up of large lexical resources. It addresses researchers that already perform text mining, and those who want to enrich their battery of methods. Selected articles can be used to support graduate-level teaching. The book is suitable for all readers that completed undergraduate studies of computational linguistics, quantitative linguistics, computer science and computational humanities. It assumes basic knowledge of computer science and corpus processing as well as of statistics.
The two-volume set LNCS 9366 and 9367 constitutes the refereed proceedings of the 14th International Semantic Web Conference, ISWC 2015, held in Bethlehem, PA, USA, in October 2015. The International Semantic Web Conference is the premier forum for Semantic Web research, where cutting edge scientific results and technological innovations are presented, where problems and solutions are discussed, and where the future of this vision is being developed. It brings together specialists in fields such as artificial intelligence, databases, social networks, distributed computing, Web engineering, information systems, human-computer interaction, natural language processing, and the social sciences. The papers cover topics such as querying with SPARQL; querying linked data; linked data; ontology-based data access; ontology alignment; reasoning; instance matching, entity resolution and topic generation; RDF data dynamics; ontology extraction and generation; knowledge graphs and scientific data publication; ontology instance alignment; knowledge graphs; data processing, IoT, sensors; archiving and publishing scientific data; I oT and sensors; experiments; evaluation; and empirical studies. Part 1 (LNCS 9366) contains a total of 38 papers which were presented in the research track. They were carefully reviewed and selected from 172 submissions. Part 2 (LNCS 9367) contains 14 papers from the in-use and software track, 8 papers from the datasets and ontologies track, and 7 papers from the empirical studies and experiments track, selected, respectively, from 33, 35, and 23 submissions.
Quantifying Language Dynamics: On the Cutting Edge of Areal and Phylogenetic Linguistics contains specially-selected papers introducing new, quantitative methodologies for understanding language interaction and evolution. It draws upon data from the phonologies, morphologies, numeral systems, constituent orders, case systems, and lexicons of the world’s languages, bringing large datasets and sophisticated statistical techniques to bear on fundamental questions such as: how to identify and account for areal distributions, when language contact leads to grammatical simplification, whether patterns of morphological borrowing can be predicted, how to deal with contact within phylogenetic models, and what new techniques are most effective for classification of the world’s languages. The book is relevant for students and scholars in general linguistics, typology, and historical and comparative linguistics.
This textbook explains Deep Learning Architecture, with applications to various NLP Tasks, including Document Classification, Machine Translation, Language Modeling, and Speech Recognition. With the widespread adoption of deep learning, natural language processing (NLP),and speech applications in many areas (including Finance, Healthcare, and Government) there is a growing need for one comprehensive resource that maps deep learning techniques to NLP and speech and provides insights into using the tools and libraries for real-world applications. Deep Learning for NLP and Speech Recognition explains recent deep learning methods applicable to NLP and speech, provides state-of-the-art approaches, and offers real-world case studies with code to provide hands-on experience. Many books focus on deep learning theory or deep learning for NLP-specific tasks while others are cookbooks for tools and libraries, but the constant flux of new algorithms, tools, frameworks, and libraries in a rapidly evolving landscape means that there are few available texts that offer the material in this book. The book is organized into three parts, aligning to different groups of readers and their expertise. The three parts are: Machine Learning, NLP, and Speech Introduction The first part has three chapters that introduce readers to the fields of NLP, speech recognition, deep learning and machine learning with basic theory and hands-on case studies using Python-based tools and libraries. Deep Learning Basics The five chapters in the second part introduce deep learning and various topics that are crucial for speech and text processing, including word embeddings, convolutional neural networks, recurrent neural networks and speech recognition basics. Theory, practical tips, state-of-the-art methods, experimentations and analysis in using the methods discussed in theory on real-world tasks. Advanced Deep Learning Techniques for Text and Speech The third part has five chapters that discuss the latest and cutting-edge research in the areas of deep learning that intersect with NLP and speech. Topics including attention mechanisms, memory augmented networks, transfer learning, multi-task learning, domain adaptation, reinforcement learning, and end-to-end deep learning for speech recognition are covered using case studies.