Probing Semantic Relations
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Probing Semantic Relations
Semantic relations are at the core of any representational system, and are keys to enable the next generation of information processing systems with semantic and reasoning capabilities. Acquisition, description, and formalization of semantic relations are fundamentals in computer-based systems where natural language processing is required. "Probing Semantic Relations" provides a state of the art of current research trends in the area of knowledge extraction from text using linguistic patterns. First published as a Special Issue of "Terminology" 14:1 (2008), the current book emphasizes how definitional knowledge is conveyed by conceptual and semantic relations such as synonymy, causality, hypernymy (generic specific), and meronymy (part whole). Showing the difficulties and successes of pattern-based approaches, the book illustrates current and future challenges in knowledge acquisition from text. This book provides new perspectives to researchers and practitioners in terminology, knowledge engineering, natural language processing, and semantics."
Probing Semantic Relations
Author: Alain Auger
language: en
Publisher: John Benjamins Publishing
Release Date: 2010-08-04
Semantic relations are at the core of any representational system, and are keys to enable the next generation of information processing systems with semantic and reasoning capabilities. Acquisition, description, and formalization of semantic relations are fundamentals in computer-based systems where natural language processing is required. Probing Semantic Relations provides a state of the art of current research trends in the area of knowledge extraction from text using linguistic patterns. First published as a Special Issue of Terminology 14:1 (2008), the current book emphasizes how definitional knowledge is conveyed by conceptual and semantic relations such as synonymy, causality, hypernymy (generic–specific), and meronymy (part–whole). Showing the difficulties and successes of pattern-based approaches, the book illustrates current and future challenges in knowledge acquisition from text. This book provides new perspectives to researchers and practitioners in terminology, knowledge engineering, natural language processing, and semantics.
Modern Computational Models of Semantic Discovery in Natural Language
Language—that is, oral or written content that references abstract concepts in subtle ways—is what sets us apart as a species, and in an age defined by such content, language has become both the fuel and the currency of our modern information society. This has posed a vexing new challenge for linguists and engineers working in the field of language-processing: how do we parse and process not just language itself, but language in vast, overwhelming quantities? Modern Computational Models of Semantic Discovery in Natural Language compiles and reviews the most prominent linguistic theories into a single source that serves as an essential reference for future solutions to one of the most important challenges of our age. This comprehensive publication benefits an audience of students and professionals, researchers, and practitioners of linguistics and language discovery. This book includes a comprehensive range of topics and chapters covering digital media, social interaction in online environments, text and data mining, language processing and translation, and contextual documentation, among others.