Overview
- Includes supplementary material: sn.pub/extras
Part of the book series: Lecture Notes in Computer Science (LNCS, volume 2953)
Part of the book sub series: Lecture Notes in Artificial Intelligence (LNAI)
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Table of contents (11 chapters)
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Front Matter
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1 Motivation
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Part I Logics
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Front Matter
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Part II Linguistics
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Front Matter
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Back Matter
Keywords
About this book
Mathematical theorem proving has undergone an impressive development during the last two decades, resulting in a variety of powerful systems for applications in mathematical deduction and knowledge processing. Natural language processing has become a topic of outstanding relevance in information technology, mainly due to the explosive growth of the Web, where by far the largest part of information is encoded in natural language documents.
This monograph focuses on the development of inference tools tailored to applications in natural language processing by demonstrating how the model generation paradigm can be used as a framework for the support of specific tasks in natural language interpretation and natural language based inference in a natural way.
The book appears at a pivotal moment, when much attention is being paid to the task of adding a semantic layer to the Web, and representation and processing of natural language based semantic information pops up as a primary requirement for further technological progress.
Authors and Affiliations
Bibliographic Information
Book Title: Model Generation for Natural Language Interpretation and Analysis
Authors: Karsten Konrad
Series Title: Lecture Notes in Computer Science
DOI: https://doi.org/10.1007/b95744
Publisher: Springer Berlin, Heidelberg
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eBook Packages: Springer Book Archive
Copyright Information: Springer-Verlag Berlin Heidelberg 2004
Softcover ISBN: 978-3-540-21069-6Published: 10 February 2004
eBook ISBN: 978-3-540-24640-4Published: 26 January 2004
Series ISSN: 0302-9743
Series E-ISSN: 1611-3349
Edition Number: 1
Number of Pages: XIV, 174
Topics: Natural Language Processing (NLP), Artificial Intelligence, Computer Science, general, Mathematical Logic and Formal Languages, Natural Language Processing (NLP)