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<article article-type="research-article" dtd-version="1.3" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xml:lang="ru"><front><journal-meta><journal-id journal-id-type="publisher-id">lingngu</journal-id><journal-title-group><journal-title xml:lang="ru">Вестник НГУ. Серия: Лингвистика и межкультурная коммуникация</journal-title><trans-title-group xml:lang="en"><trans-title>NSU Vestnik. Series: Linguistics and Intercultural Communication</trans-title></trans-title-group></journal-title-group><issn pub-type="ppub">1818-7935</issn><publisher><publisher-name>Новосибирский государственный университет</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="doi">10.25205/1818-7935-2022-20-3-64-74</article-id><article-id custom-type="elpub" pub-id-type="custom">lingngu-416</article-id><article-categories><subj-group subj-group-type="heading"><subject>Research Article</subject></subj-group><subj-group subj-group-type="section-heading" xml:lang="ru"><subject>ПРИКЛАДНАЯ ЛИНГВИСТИКА</subject></subj-group><subj-group subj-group-type="section-heading" xml:lang="en"><subject>APPLIED LINGUISTICS</subject></subj-group></article-categories><title-group><article-title>К вопросу о частотностях глагольных конструкций в некоторых жанрах массовой литературы</article-title><trans-title-group xml:lang="en"><trans-title>On Verbal Construction Frequency in Certain Genres of Popular Literature</trans-title></trans-title-group></title-group><contrib-group><contrib contrib-type="author" corresp="yes"><contrib-id contrib-id-type="orcid">https://orcid.org/0000-0002-0604-6603</contrib-id><name-alternatives><name name-style="eastern" xml:lang="ru"><surname>Буйлова</surname><given-names>Н. Н.</given-names></name><name name-style="western" xml:lang="en"><surname>Buylova</surname><given-names>N. N.</given-names></name></name-alternatives><bio xml:lang="ru"><p>Москва</p></bio><bio xml:lang="en"><p>Nadezhda N. Bujlova, junior researcher </p><p>Moscow</p></bio><email xlink:type="simple">nbujlova@ruscorpora.ru</email><xref ref-type="aff" rid="aff-1"/></contrib><contrib contrib-type="author" corresp="yes"><contrib-id contrib-id-type="orcid">https://orcid.org/0000-0001-8374-423X</contrib-id><name-alternatives><name name-style="eastern" xml:lang="ru"><surname>Ляшевская</surname><given-names>О. Н.</given-names></name><name name-style="western" xml:lang="en"><surname>Lyashevskaya</surname><given-names>O. N.</given-names></name></name-alternatives><bio xml:lang="ru"><p>Москва</p></bio><bio xml:lang="en"><p>Olga N. Lyashevskaya, Doctor of Philosophy, Professor </p><p>Moscow</p></bio><email xlink:type="simple">olesar@yandex.ru</email><xref ref-type="aff" rid="aff-2"/></contrib></contrib-group><aff-alternatives id="aff-1"><aff xml:lang="ru"><institution>Институт проблем передачи информации им. Харкевича РАН</institution><country>Россия</country></aff><aff xml:lang="en"><institution>The Institute for Information Transmission Problems RAS (Kharkevich Institute)</institution><country>Russian Federation</country></aff></aff-alternatives><aff-alternatives id="aff-2"><aff xml:lang="ru"><institution>Национальный исследовательский университет «Высшая школа экономики»; Институт русского языка им. Виноградова РАН</institution><country>Россия</country></aff><aff xml:lang="en"><institution>HSE University; The Russian Language Institute RAS (Vinogradov Institute)</institution><country>Russian Federation</country></aff></aff-alternatives><pub-date pub-type="collection"><year>2022</year></pub-date><pub-date pub-type="epub"><day>14</day><month>10</month><year>2022</year></pub-date><volume>20</volume><issue>3</issue><fpage>64</fpage><lpage>74</lpage><permissions><copyright-statement>Copyright &amp;#x00A9; Буйлова Н.Н., Ляшевская О.Н., 2022</copyright-statement><copyright-year>2022</copyright-year><copyright-holder xml:lang="ru">Буйлова Н.Н., Ляшевская О.Н.</copyright-holder><copyright-holder xml:lang="en">Buylova N.N., Lyashevskaya O.N.</copyright-holder><license xml:lang="ru" license-type="creative-commons-attribution" xlink:href="https://creativecommons.org/licenses/by/4.0/" xlink:type="simple"><license-p>Данная работа распространяется под лицензией Creative Commons Attribution 4.0.</license-p></license><license xml:lang="en" license-type="creative-commons-attribution" xlink:href="https://creativecommons.org/licenses/by/4.0/" xlink:type="simple"><license-p>This work is licensed under a Creative Commons Attribution 4.0 License.</license-p></license></permissions><self-uri xlink:href="https://lingngu.elpub.ru/jour/article/view/416">https://lingngu.elpub.ru/jour/article/view/416</self-uri><abstract><p>Использование корпусов для изучения специфических областей языка – широко распространенная практика в современной лингвистике. В нашем исследовании мы обращаемся к такому лингвокультурологическому явлению, как массовая литература (бульварный роман). Клишированность такой литературы позволяет описывать ее при помощи так называемых литературных формул, в универсальной форме воплощающих культурные темы и стереотипы социума. В основном исследуемые культурологами и литературоведами, с лингвистической точки зрения литературные формулы изучены недостаточно. В работе мы рассматриваем четыре микрожанра1 (любовный роман, детектив, научно-фантастический роман и фэнтези), которые примерно соответствуют литературным формулам «романтическая история», «тайна», «чуждые сущности и состояния» и «приключения». Поскольку низкоуровневые признаки (длина текста, длина слова, частеречные характеристики) не дают однозначного ответа относительно принадлежности текста к той или иной литературной формуле, в работе на материале корпуса текстов массовой литературы анализируются глагольные конструкции (комплексы вида «глагол + зависимые»), характерные для одного или нескольких микрожанров. Для выделения подобных комплексов используются методы машинного обучения. Конструкции, внесшие наибольший вклад в кластеризацию по жанрам, мы называем маркерными, т. е. характерными для определенного микрожанра. Конструкции рассматриваются с точки зрения заполненности валентностей глагола. В исследовании выделяются полные (словарные), неполные (с опущением субъекта, прямого или косвенного объекта) и расширенные (сирконстантные) глагольные конструкции. Проанализированы и описаны конкретные примеры реализации конструкций в каждом из микрожанров. На основе схожести конструкционного профиля сделаны выводы о преобладании в любовных романах и детективах глаголов и конструкций, сопутствующих прямой речи, и о превалировании полных конструкций в научно-фантастических романах и фэнтези. Предложены также внетекстовые объяснения подобного предпочтения. Предложенный в исследовании метод может быть использован для изучения синтаксических особенностей различного генеза: обусловленных авторской, временной или жанровой спецификой.</p></abstract><trans-abstract xml:lang="en"><p>Corpora-based language studies is a widespread practice in modern linguistics. In our study, we address so-called mass literature (or paraliterature) via a corpus of texts. The standardization of mass literature allows us to describe its genres by applying literary formulas. In brief, a formula serves for the embodiment of cultural themes and stereotypes in a universal form. While mass literature is a common subject of literary and cultural studies, from a linguistic point of view, literary formulas have not been studied well enough. We suggest that differences between the microgenres of paraliterature may be in syntax as well as in the vocabulary. Our work is based on the mass literature corpora and provides analysis of verb constructions characteristic of microgenres (love story, detective story, science fiction novel, and fantasy). In order to identify the distinctive features of mass literature microgenres, we have conducted a series of machine learning experiments. As a dataset, we compiled a corpus of 1,200 texts belonging to four microgenres. In our previous studies we showed that statistical features (text length, sentence length, and parts of speech frequencies) were insufficient for successful classification. The usage of lexical features has improved the quality of machine learning, however, the classifier based on syntactic features has shown the best results. 81 constructions have been selected as the most important features of syntactic-based machine learning. A verb construction consists of a “verb + dependencies”. We consider several types of dependencies: arguments (subject, direct and indirect objects) and adjuncts (subordinate clause and adverbial classifier). The constructions are described in terms of the fullness of the verb valencies. Several types of constructions are distinguished: complete, incomplete (with omissions of direct or indirect complements), and extended (with adjuncts). Specific examples of verb constructions in each of the microgenres have been analyzed and described. Based on the similarity of the construction profile, romance novels and detective stories show the predominance of verbs and direct speech constructions, whereas science fiction novels and fantasy demonstrate the prevalence of full constructions. Possible non-textual explanations for our observations are also provided. The method proposed in the study can be used for the investigation of various kinds of syntactic features, for instance, those associated with the author’s, temporal or genre specificities.</p></trans-abstract><kwd-group xml:lang="ru"><kwd>массовая литература</kwd><kwd>литературная формула</kwd><kwd>формульная литература</kwd><kwd>глагольные конструкции</kwd><kwd>синтаксические маркеры</kwd></kwd-group><kwd-group xml:lang="en"><kwd>paraliterature</kwd><kwd>formula literature</kwd><kwd>verb constructions</kwd><kwd>syntactic markers</kwd></kwd-group></article-meta></front><back><ref-list><title>References</title><ref id="cit1"><label>1</label><citation-alternatives><mixed-citation xml:lang="ru">Апресян Ю. Д. Лексические конверсивы в русском языке // Linguistica Silesiana, 1975. Т. 1. С. 256–283.</mixed-citation><mixed-citation xml:lang="en">Apresyan, Yu. D. Lexical conversion in Russian. Linguistica Silesiana, 1975, 1, pp. 256–283. 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