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Возможности автоматизированного выделения гипо-гиперонимических пар из словарных определений глаголов

Authors: Antropova, O.l.; Ogorodnikova, E.A.;

Возможности автоматизированного выделения гипо-гиперонимических пар из словарных определений глаголов

Abstract

Антропова Оксана Игоревна, старший преподаватель кафедры технической физики, Уральский федеральный университет, choksy@mail.ru Огородникова Екатерина Алексеевна, ассистент кафедры лингвистики и профессиональной коммуникации на иностранных языках, Уральский федеральный университет, kruglikova.katya@yandex.ru Oksana I. Antropova, Senior Lecturer of the Chair of Technical Physics, Ural Federal University named after B.N. Yeltsin (Ekaterinburg), choksy@mail.ru Ekaterina A. Ogorodnikova, Teaching Assistant of the Chair of Linguistics and Professional Communication on Foreign Languages, Ural Federal University named after B.N. Yeltsin (Ekaterinburg), kruglikova.katya@yandex.ru В статье рассматриваются методы автоматизированного выявления семантических отношений между единицами языка. Целью работы является разработка метода извлечения родовидовых глагольных отношений из словарных дефиниций. Предлагаемый метод основывается на идее лексико-синтаксических шаблонов и его адаптации к глагольной лексике. В ходе работы проанализированы определения из шести словарей, из дефиниций с помощью предварительно определенных лексических маркеров извлечены предполагаемые родо-видовые пары. Результат автоматической обработки проверен вручную. Проанализированы результаты применения метода, наиболее частотные ошибки, допущенные в ходе компьютерной обработки, а также представлены возможные пути повышения качества работы программы. Первичная апробация метода показала, что его дальнейшее развитие и уточнение имеет большую перспективу, и он может стать основой для создания программы автоматического установления родо-видовых отношений, показывающей высокую точность. Результаты работы могут быть использованы в различных областях прикладной лингвистики, а также при дальнейшем развитии теоретической семантики. The paper concerns computer-aided methods for extraction of semantic relations between lexical units. The aim of the research is to elaborate a method of genus-species relations extraction from dictionary definitions. The suggested method is based on the idea of lexico-syntactic patterns and its adaptation to verbal vocabulary. Definitions from six dictionaries are analyzed, probable genus-species pairs are extracted from the definitions with the help of previously defined lexical markers. The result of automatic processing is verified manually. The results of the method application and the most frequent mistakes made during computer processing are analyzed and possible ways of method improvement are suggested. The primary approbation of the method showed that its further development and elaboration have good prospects, and it can be a base for creation of an automatic genus-species relations extraction software showing high accuracy. The results of the research can be used in different domains of computational linguistics and during further research in theoretical semantics. Исследование выполнено при финансовой поддержке РФФИ в рамках научного проекта № 18-312-00129.

Keywords

семантика, troponymy, verb, глагол, лексикография, lexico-syntactic patterns, lexicography, гипонимия, лексико-синтаксические шаблоны, hyponymy, УДК 81-13, semantics, тропонимия

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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
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