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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.
In this modern time of the Internet, information is just a click away. While it may be tempting to regard information as an object or an end-product, the acquisition of information is only the start to the process of gaining knowledge. This book proposes and describes the heart model and information hierarchy as a means to explain information as a process of gaining useful knowledge. This provides an effective approach to everyday decision-making and problem solving.
At its core, machine learning is about efficiently identifying patterns and relationships in data. Many tasks, such as finding associations among terms so you can make accurate search recommendations or locating individuals within a social network who have similar interests, are naturally expressed as graphs. Graph-Powered Machine Learning introduces you to graph technology concepts, highlighting the role of graphs in machine learning and big data platforms. You'll get an in-depth look at techniques including data source modeling, algorithm design, link analysis, classification, and clustering. As you master the core concepts, you'll explore three end-to-end projects that illustrate architectures, best design practices, optimization approaches, and common pitfalls. Key Features · The lifecycle of a machine learning project · Three end-to-end applications · Graphs in big data platforms · Data source modeling · Natural language processing, recommendations, and relevant search · Optimization methods Readers comfortable with machine learning basics. About the technology By organizing and analyzing your data as graphs, your applications work more fluidly with graph-centric algorithms like nearest neighbor or page rank where it's important to quickly identify and exploit relevant relationships. Modern graph data stores, like Neo4j or Amazon Neptune, are readily available tools that support graph-powered machine learning. Alessandro Negro is a Chief Scientist at GraphAware. With extensive experience in software development, software architecture, and data management, he has been a speaker at many conferences, such as Java One, Oracle Open World, and Graph Connect. He holds a Ph.D. in Computer Science and has authored several publications on graph-based machine learning.
The Segment in Phonetics and Phonology unravels exactly what the segment is and on what levels it exists, approaching the study of the segment with theoretical, empirical, and methodological heterogeneity as its guiding principle. A deliberately eclectic approach to the study of the segment that investigates exactly what the segment is and on what level it exists Includes new research data from a diverse range of fields such as experimental psycholinguistics, language acquisition, and mathematical theories of communication Represents the major theoretical models of phonology, including Articulatory Phonology, Optimality Theory, Laboratory Phonology and Generative Phonology Examines both well-studied languages like English, Chinese, and Japanese and under-studied languages such as Southern Sierra Miwok, Päri, and American Sign Language
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The essential one-volume resource for advanced students and academics in phonology. >
Each issue lists papers published during the preceding year.