Memory-based language processing--a machine learning and problem solving method for language technology--is based on the idea that the direct re-use of examples using analogical reasoning is more suited for solving language processing problems than the application of rules extracted from those examples. This book discusses the theory and practice of memory-based language processing, showing its comparative strengths over alternative methods of language modelling. Language is complex, with few generalizations, many sub-regularities and exceptions, and the advantage of memory-based language processing is that it does not abstract away from this valuable low-frequency information.
Memory-Based Language Processing
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Book Description
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Book Description
This book discusses the theory and practice of memory-based language processing--a machine learning and problem solving method for language technology--showing its comparative strengths over alternative methods of language modelling. By applying the model to a range of benchmark problems, the authors show the excellent results it can produce for a range of linguistic areas from phonology to semantics. They also describe TiMBL, a software package for memory-based language processing. The first comprehensive overview of the approach, this book will be invaluable for computational linguists, psycholinguists and language engineers.
About the Author
Walter Daelemans is Professor of Computational Linguistics and AI in the Department of Linguistics, University of Antwerp.
Antal van den Bosch is Assistant Professor in the Department of Computational Linguistics and AI, Tilburg University.
Antal van den Bosch is Assistant Professor in the Department of Computational Linguistics and AI, Tilburg University.
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Inside This Book
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First Sentence:
This book presents a simple and efficient approach to solving natural language processing problems. Read the first page Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
correctly classified test instances, class trigrams, phrase chunking, analogical modeling, cps value, verb attachment, plural data, generalization accuracy, editing experiments, progressive sampling, generalization accuracies, unseen words, feature weighting, shallow parser, shallow parsing, prepositional phrase attachment, generalizing examples, windowing process, natural language learning, generalization performance, neighbor letters, benchmark tasks, eager learning, density block, lazy learning Key Phrases - Capitalized Phrases (CAPs): (learn more)
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This book presents a simple and efficient approach to solving natural language processing problems. Read the first page Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
correctly classified test instances, class trigrams, phrase chunking, analogical modeling, cps value, verb attachment, plural data, generalization accuracy, editing experiments, progressive sampling, generalization accuracies, unseen words, feature weighting, shallow parser, shallow parsing, prepositional phrase attachment, generalizing examples, windowing process, natural language learning, generalization performance, neighbor letters, benchmark tasks, eager learning, density block, lazy learning Key Phrases - Capitalized Phrases (CAPs): (learn more)
Tue Dec, Tjong Kim Sang New!
Books on Related Topics | Concordance | Text Stats Browse Sample Pages:
Front Cover | Table of Contents | First Pages | Index | Back Cover | Surprise Me!
Citations (learn more)
This book cites 19
books:
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- Computational Linguistics: An Introduction (Studies in Natural Language Processing) by Ralph Grishman in Front Matter, and Back Matter
- in Back Matter (1), and Back Matter (2)
- Problem Solving by Frank Honywill George in Front Matter
- A Computational Theory of Writing Systems (Studies in Natural Language Processing) by Richard Sproat in Front Matter
- Challenges in Natural Language Processing (Studies in Natural Language Processing) by Madeleine Bates in Front Matter
See all 19 books this book cites
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