ะะทะดะฐัะตะปัััะฒะพ Marcel Dekker, 2000, -962 pp.
The discipline of Natural Language Processing (NLP) concerns itself with the design and implementation of computational machinery that communicates with humans using natural language. Why is it important to pursue such an endeavor? Given the self-evident observation that humans communicate most easily and effectively with one another using natural language, it follows that, in principle, it is the easiest and most effective way for humans and machines to interact; as technology proliferates around us, that interaction will be increasingly important. At its most ambitious, NLP research aims to design the language input- output components of artificially intelligent systems that are capable of using language as fluently and flexibly as humans do. The robots of science fiction are archetypical: stronger and more intelligent than their creators and having access to vastly greater knowledge, but human in their mastery of language. Even the most ardent exponent of artificial intelligence research would have to admit that the likes of HAL in Kubrick's 2001: A Space Odyssey remain firmly in the realms of science fiction. Some success, however, has been achieved in less ambitious domains, where the research problems are more precisely definable and therefore more tractable. Machine translation is such a domain, and one that finds ready application in the internationalism of contemporary economic, political, and cultural life. Other successful areas of application are message-understanding systems, which extract useful elements of the propositional content of textual data sources such as newswire reports or banking telexes, and front ends for information systems such as databases, in which queries can be framed and replies given in natural language. This handbook is about the design of these and other sorts of NLP systems. Throughout, the emphasis is on practical tools and techniques for implementable systems; speculative research is minimized, and polemic excluded.
Part I Symbolic Approaches to NLP.
Symbolic Approaches to Natural Language Processing.
Tokenisation and Sentence Segmentation.
Lexical Analysis.
Parsing Techniques.
Semantic Analysis.
Discourse Structure and Intention Recognition.
Natural Language Generation.
Intelligent Writing Assistance.
Database Interfaces.
Information Extraction.
The Generation of Reports from Databases.
The Generation of Multimedia Presentations.
Machine Translation.
Dialogue Systems: From Theory to Practice in TRAINS-96.
Part II Empirical Approaches to NLP.
Empirical Approaches to Natural Language Processing.
Corpus Creation for Data-Intensive Linguistics.
Part-of-Speech Tagging.
Alignment.
Contextual Word Similarity.
Computing Similarity.
Collocations.
Statistical Parsing.
Authorship Identification and Computational Stylometry.
Lexical Knowledge Acquisition.
Example-Based Machine Translation.
Word-Sense Disambiguation.
Part III Artificial Neural Network Approaches to NLP.
NLP Based on Artificial Neural Networks: Introduction.
Knowledge Representation.
Grammar Inference, Automata Induction, and Language Acquisition.
The Symbolic Approach to ANN-Based Natural Language Processing.
The Subsymbolic Approach to ANN-Based Natural Language Processing.
The Hybrid Approach to ANN-Based Natural Language Processing.
Character Recognition with Syntactic Neural Networks.
Compressing Texts with Neural Nets.
Neural Architectures for Information Retrieval and Database Query.
Text Data Mining.
Text and Discourse Understanding: The DISCERN System.