This book provides a self-contained introduction to Learning Language in Logic (LLL) and, at the same time, competently surveys the current state of the art of research in the area. The book has its origin in the first LLL workshop which took place in Bled, Slovenia in June 1999. Beyond revised workshop papers, a number of additional chapters were solicited from leading researchers in order to achieve complete coverage of all current aspects and to introduce the new area to a broader audience. As the first book devoted to LLL, this thoroughly cross-reviewed state-of-the-art survey will become a valuable source of reference for researchers active in the area. Newcomers or professionals interested in advanced LLL applications will find this book an ideal guide to an exciting new field.
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First Sentence:
Learning Language in Logic (LLL) is an emerging research area lying at the intersection of computational logic, machine learning and natural language processing (Fig 1). Read the first page Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
recursive transfer rules, sigma edge, first order explosion, unannotated data, orthographic data, statistical inference algorithm, suffix clauses, unparsable sentence, clause inference rule, iterative induction, learning information extraction rules, predicate schemata, prefix clauses, bottom clause, parsed corpora, most probable parse, clause daughter, unsupervised learner, verb subcategorization frames, linguistic background knowledge, probable parses, unknown nouns, substructural logic, morphological analyser, parsed corpus Key Phrases - Capitalized Phrases (CAPs): (learn more)
International Workshop, Springer-Verlag Berlin Heidelberg, Della Pietra, Tjong Kim Sang, Springer Verlag, International Conference, New York, Menlo Park, Naive Bayes, Lecture Notes, Morgan Kaufman, Training Examples Fig, Cambridge University Press, General Linguistics, Induction of Recursive Transfer Rules, San Francisco, The Netherlands, Very Large Corpora, Cognitive Science, Err Accuracy Correct, Stanford University, Theory of Morphology, University of Groningen, University of Texas, University of York New!
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Front Cover | Table of Contents | First Pages | Index | Back Cover | Surprise Me!
Learning Language in Logic (LLL) is an emerging research area lying at the intersection of computational logic, machine learning and natural language processing (Fig 1). Read the first page Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
recursive transfer rules, sigma edge, first order explosion, unannotated data, orthographic data, statistical inference algorithm, suffix clauses, unparsable sentence, clause inference rule, iterative induction, learning information extraction rules, predicate schemata, prefix clauses, bottom clause, parsed corpora, most probable parse, clause daughter, unsupervised learner, verb subcategorization frames, linguistic background knowledge, probable parses, unknown nouns, substructural logic, morphological analyser, parsed corpus Key Phrases - Capitalized Phrases (CAPs): (learn more)
International Workshop, Springer-Verlag Berlin Heidelberg, Della Pietra, Tjong Kim Sang, Springer Verlag, International Conference, New York, Menlo Park, Naive Bayes, Lecture Notes, Morgan Kaufman, Training Examples Fig, Cambridge University Press, General Linguistics, Induction of Recursive Transfer Rules, San Francisco, The Netherlands, Very Large Corpora, Cognitive Science, Err Accuracy Correct, Stanford University, Theory of Morphology, University of Groningen, University of Texas, University of York 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 60
books:
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62
books
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- Inductive Logic Programming (Apic Studies in Data Processing) by Stephen Muggleton on 13 pages
- Computational Linguistics: An Introduction (Studies in Natural Language Processing) by Ralph Grishman on 4 pages
- Statistical Language Learning (Language, Speech, and Communication) by Eugene Charniak on page 47, page 63, and page 232
- Foundations of Statistical Natural Language Processing by Christopher D. Manning on page 48, and page 64
- Machine Intelligence by B. Meltzer on page 34, and Back Matter
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- Problem Solving Methods by J. Siekmann in Back Matter (1), and Back Matter (2)
- Advances in Robot Learning: 8th European Workhop on Learning Robots, EWLR-8 Lausanne, Switzerland, September 18, 1999 Proceedings (Lecture Notes in ... / Lecture Notes in Artificial Intelligence) by Jeremy Wyatt in Back Matter (1), and Back Matter (2)
- Envisioning Machine Translation in the Information Future : 4th Conference of the Association for Machine Translation in the Americas, AMTA 2000 Cuernavaca, Mexico by John S. White in Back Matter (1), and Back Matter (2)
- Algorithmic Learning Theory: 11th International Conference, ALT 2000 Sydney, Australia, December 11-13, 2000 Proceedings (Lecture Notes in Computer Science / Lecture Notes in Artificial Intelligence) by Hiroki Arimura in Back Matter (1), and Back Matter (2)
- Policies for Distributed Systems and Networks: International Workshop, POLICY 2001 Bristol, UK, January 29-31, 2001 Proceedings (Lecture Notes in Computer Science) by Morris Sloman in Back Matter
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