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By Hermann Helbig

The publication provides an interdisciplinary method of wisdom illustration and the remedy of semantic phenomena of normal language, that is situated among synthetic intelligence, computational linguistics, and cognitive psychology. The proposed procedure is predicated on Multilayered prolonged Semantic Networks (MultiNets), which are used for theoretical investigations into the semantics of ordinary language, for cognitive modeling, for describing lexical entries in a computational lexicon, and for ordinary language processing (NLP).

Part I bargains with primary difficulties of semantic wisdom illustration and semantic interpretation of usual language phenomena. half II presents a scientific description of the representational technique of MultiNet, the most complete and punctiliously certain collections of family members and features utilized in genuine NLP applications.

MultiNet is embedded right into a process of software program instruments comprising a workbench for the data engineer, a semantic interpreter translating usual language expressions into formal which means constructions, and a workbench for the pc lexicographer. The booklet has been used for classes in synthetic intelligence at a number of universities and is among the cornerstones for educating computational linguistics in a digital digital laboratory.

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Additional resources for Knowledge representation and the semantics of natural language: with 258 figures, 23 tables and CD-ROM

Example text

5 gives an overview of the different dimensions spanned by attributes according to which the nodes of a semantic network can be categorized. The following dimensions are shown: degree of generality GENER (with the attribute values: generalized – ge and specific – sp), determination of reference REFER (with the attribute values: determined – det and not determined – indet), facticity FACT (with the attribute values: real – real, hypothetical – hypo, nonreal – nonreal or, for short, non), degree of variability VARIA (with the attribute values: variable – var, constant – con) and CARD (with natural numbers as values).

On the other hand, there are concepts where only the first two components are encountered. g. righteousness, determinism, etc. With semantic networks we try to describe just the first two components. Neural Networks, in contrast, are very suitable for modeling the third aspect [217, 143, 284]. However, they largely lack the inclusion of the first two components. It is hoped that essential progress in the field of knowledge representation and cognitive modeling can be achieved by the unification of both lines of development (a goal that lies still ahead of AI).

Nevertheless, the PROP characterization stays an immanent property of G02 (expressed by the label categ). The analogue holds for G01. The situational knowledge G01 had been sold must be positioned outside the capsule of G01 from the very beginning, since an act of selling does not determine the concept G01 or a house in general (neither in the immanent sense nor in the definitional sense). ” with “The discoverer of nuclear fission”. 2 Classificatory Knowledge 35 and G04 illustrate an analogous situation with the use of prepositional phrases or restrictive relative clauses on the one hand and predicative constructs on the other hand.

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