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Routledge Q&As give you the tools to practice and refine your exam technique, showing you how to apply your knowledge to maximum effect in assessment. Each book contains essay and problem-based questions on the most commonly examined topics, complete with expert guidance and model answers that help you to: Plan your revision and know what examiners are looking for: Introducing how best to approach revision in each subject Identifying and explaining the main elements of each question, and providing marker annotation to show how examiners will read your answer Understand and remember the law: Using memorable diagram overviews for each answer to demonstrate how the law fits together and how best to structure your answer Gain marks and understand areas of debate: Providing revision tips and advice to help you aim higher in essays and exams Highlighting areas that are contentious and on which you will need to form an opinion Avoid common errors: Identifying common pitfalls students encounter in class and in assessment The series is supported by an online resource that allows you to test your progress during the run-up to exams. Features include: multiple choice questions, bonus Q&As and podcasts.
Routledge Q&As give you the tools to practice and refine your exam technique, showing you how to apply your knowledge to maximum effect in assessment. Each book contains essay and problem-based questions on the most commonly examined topics, complete with expert guidance and model answers that help you to: Plan your revision and know what examiners are looking for: Introducing how best to approach revision in each subject Identifying and explaining the main elements of each question, and providing marker annotation to show how examiners will read your answer Understand and remember the law: Using memorable diagram overviews for each answer to demonstrate how the law fits together and how best to structure your answer Gain marks and understand areas of debate: Providing revision tips and advice to help you aim higher in essays and exams Highlighting areas that are contentious and on which you will need to form an opinion Avoid common errors: Identifying common pitfalls students encounter in class and in assessment The series is supported by an online resource that allows you to test your progress during the run-up to exams. Features include: multiple choice questions, bonus Q&As and podcasts.
This book constitutes the refereed proceedings of the 22nd Australasian Joint Conference on Artificial Intelligence, AI 2009, held in Melbourne, Australia, in December 2009. The 68 revised full papers presented were carefully reviewed and selected from 174 submissions. The papers are organized in topical sections on agents; AI applications; computer vision and image processing; data mining and statistical learning; evolutionary computing; game playing; knowledge representation and reasoning; natural language and speech processing; soft computing; and user modelling.
The Handbook of Natural Language Processing, Second Edition presents practical tools and techniques for implementing natural language processing in computer systems. Along with removing outdated material, this edition updates every chapter and expands the content to include emerging areas, such as sentiment analysis. New to the Second Edition Greater prominence of statistical approaches New applications section Broader multilingual scope to include Asian and European languages, along with English An actively maintained wiki (http://handbookofnlp.cse.unsw.edu.au) that provides online resources, supplementary information, and up-to-date developments Divided into three sections, the book first surveys classical techniques, including both symbolic and empirical approaches. The second section focuses on statistical approaches in natural language processing. In the final section of the book, each chapter describes a particular class of application, from Chinese machine translation to information visualization to ontology construction to biomedical text mining. Fully updated with the latest developments in the field, this comprehensive, modern handbook emphasizes how to implement practical language processing tools in computational systems.
This book constitutes the refereed proceedings of the 9th International Conference on Machine Learning and Data Mining in Pattern Recognition, MLDM 2013, held in New York, USA in July 2013. The 51 revised full papers presented were carefully reviewed and selected from 212 submissions. The papers cover the topics ranging from theoretical topics for classification, clustering, association rule and pattern mining to specific data mining methods for the different multimedia data types such as image mining, text mining, video mining and web mining.
"This book provides relevant theoretical frameworks and the latest empirical research findings in biomedicine information retrieval as it pertains to linguistic granularity"--Provided by publisher.
The field of artificial intelligence (AI) and the law is on the cusp of a revolution that began with text analytic programs like IBM's Watson and Debater and the open-source information management architectures on which they are based. Today, new legal applications are beginning to appear and this book - designed to explain computational processes to non-programmers - describes how they will change the practice of law, specifically by connecting computational models of legal reasoning directly with legal text, generating arguments for and against particular outcomes, predicting outcomes and explaining these predictions with reasons that legal professionals will be able to evaluate for themselves. These legal applications will support conceptual legal information retrieval and allow cognitive computing, enabling a collaboration between humans and computers in which each does what it can do best. Anyone interested in how AI is changing the practice of law should read this illuminating work.

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