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By Hermann Helbig

The ebook provides an interdisciplinary method of wisdom illustration and the therapy of semantic phenomena of typical language, that's situated among synthetic intelligence, computational linguistics, and cognitive psychology. The proposed process is predicated on Multilayered prolonged Semantic Networks (MultiNets), that are used for theoretical investigations into the semantics of usual language, for cognitive modeling, for describing lexical entries in a computational lexicon, and for common language processing (NLP).

Part I offers with primary difficulties of semantic wisdom illustration and semantic interpretation of normal language phenomena. half II presents a scientific description of the representational technique of MultiNet, the most finished and carefully distinct collections of kin and features utilized in genuine NLP applications.

MultiNet is embedded right into a method of software program instruments comprising a workbench for the data engineer, a semantic interpreter translating usual language expressions into formal that means constructions, and a workbench for the pc lexicographer. The publication has been used for classes in man made intelligence at numerous universities and is without doubt one of the cornerstones for instructing computational linguistics in a digital digital laboratory.

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

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Examples are given in Figs. 8 of Part II. A first orientation for the assignment of a certain knowledge type to an arc is given in Appendix D. e. e. immanent) knowledge with regard to k½ , while the incoming 30 3. Basic Concepts AGT arc at the second argument k¾ bears a situational character with regard to k¾ . 2. The different knowledge types and their names As specified in the table of Appendix D, relations such as the lexical relations, the subordination relations SUB, SUBS, or certain relations characterizing objects (among them, PARS, ORIGM, and ATTR), and the comparison relations are typical of the immanent knowledge with regard to their first arguments.

9 One can generally state that the automatic discrimination between immanent and situational knowledge (and, additionally, between definitional and assertional knowledge, see below) is an unsolved problem within computational linguistics. ” This sentence demonstrates how closely interwoven immanent and situational knowledge are, and what problems arise if one has to distinguish them from each other by means of an automatic language processing system. The situations represented by the nodes C01 and C02 in Fig.

E. there is more than one node of the network functioning as kernel of the answer. In this case, a whole text (an essay) has to be generated as an answer on the basis of a partial network retrieved during questionanswering from the knowledge base. Depending on the question type, different inference methods have to be used for knowledge extraction and also for answer generation. This is also an important distinguishing feature for the semantic and pragmatic interpretation of different questions.

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