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Метод автоматической идентификации семантических корреляций терминов глоссария

Метод автоматической идентификации семантических корреляций терминов глоссария

Abstract

В работе предлагается метод автоматической идентификации концептов и их отношений для построения семантической сети предметной области. Рассматриваются семантические корреляции терминов глоссария с точки зрения возможности экстракции и идентификации концептов и их отношений. Предложенная математическая модель позволяет выделить классы толерантности терминов за счет факторизации пространства концептов. Для формализации категорий межконцептуальных отношений узлов семантической сети предлагается использовать диапазон значений коэффициента семантической близости и шаблоны лексических последовательностей. The paper proposes the method of the automatic identification of concepts and their relations for building a domain semantic network. We consider semantic correlations between glossary terms from the viewpoint of possibility of the extraction and identification of concepts and their relations. The proposed mathematical model allows to identify classes of tolerance of the terms due to the factorization of the space of concepts. To formalize the categories of relations between semantic network nodes a range of values of the coefficient of semantic proximity and patterns of lexical sequences are encouraged to use.

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Keywords

классы толерантности, идентификация отношений концептов, identification of relations of concepts, semantic proximity, семантические сети, semantic network, glossary, tolerance classes, relations of concepts, семантическая близость, межконцептуальные отношения, глоссарий

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
Green