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The Advantages of Using SK–languages for Designing Semantic-Syntactic Analyzers of Recommender Systems
The paper describes a broadly applicable method of designing multilingual semantics-syntactic analyzers of recommender systems. The user inputs may include the questions of many kinds formed with the help of interrogative words (or without interrogative words), verbs, nouns, attributes, prepositions, the designations of the digital values of various parameters. For the queries in English and German, the developed algorithm of semantic-syntactic analysis processes the questions of many kinds, the commands, and the statements from a restricted sublanguage of natural language (NL). For the queries in Russian, the algorithm is additionally able to process the requests with participle constructions and attributive clauses. As a semantic intermediary language, the algorithm uses the SK-language determined by the considered linguistic database. The class of SK-languages is introduced by the theory of K-representations (knowledge representations) - an original theory of designing semantic-syntactic analyzers of NL-texts with the broad use of formal means for representing input, intermediary, and output data. The current version of this theory is stated in the monograph of the author published by Springer in 2010. The developed algorithm is implemented by means of the programming language PYTHON.