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Books > Computing & IT > Applications of computing > Artificial intelligence > Natural language & machine translation
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Lexical Semantics and Knowledge Representation in Multilingual Text Generation (Hardcover, 1999 ed.)
Loot Price: R4,483
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Lexical Semantics and Knowledge Representation in Multilingual Text Generation (Hardcover, 1999 ed.)
Series: The Springer International Series in Engineering and Computer Science, 492
Expected to ship within 10 - 15 working days
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In knowledge-based natural language generation, issues of formal
knowledge representation meet with the linguistic problems of
choosing the most appropriate verbalization in a particular
situation of utterance. Lexical Semantics and Knowledge
Representation in Multilingual Text Generation presents a new
approach to systematically linking the realms of lexical semantics
and knowledge represented in a description logic. For language
generation from such abstract representations, lexicalization is
taken as the central step: when choosing words that cover the
various parts of the content representation, the principal
decisions on conveying the intended meaning are made. A preference
mechanism is used to construct the utterance that is best tailored
to parameters representing the context. Lexical Semantics and
Knowledge Representation in Multilingual Text Generation develops
the means for systematically deriving a set of paraphrases from the
same underlying representation with the emphasis on events and verb
meaning. Furthermore, the same mapping mechanism is used to achieve
multilingual generation: English and German output are produced in
parallel, on the basis of an adequate division between
language-neutral and language-specific (lexical and grammatical)
knowledge. Lexical Semantics and Knowledge Representation in
Multilingual Text Generation provides detailed insights into
designing the representations and organizing the generation
process. Readers with a background in artificial intelligence,
cognitive science, knowledge representation, linguistics, or
natural language processing will find a model of language
production that can be adapted to a variety of purposes.
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