maisМоделирование и анализ информационных системModeling and Analysis of Information Systems1818-10152313-5417Yaroslavl State University10.18255/1818-1015-2024-3-226-239mais-1876Research ArticleArtificial IntelligenceМетоды определения неявно упоминаемых аспектов в публицистических предложениях на русском языкеMethods of implicit aspect detection in Russian publicism sentenceshttps://orcid.org/0000-0003-0116-4739ПолетаевАнатолий ЮрьевичPoletaevAnatoliy Y.anatoliy-poletaev@mail.ruhttps://orcid.org/0000-0003-3984-8423ПарамоновИлья ВячеславовичParamonovIlya V.ilya.paramonov@fruct.orghttps://orcid.org/0009-0006-4312-2413КолупаевЕгор МихайловичKolupaevEgor M.kolupaew.eg@yandex.ruЯрославский государственный университет им. П.Г. ДемидоваРоссияP.G. Demidov Yaroslavl State UniversityRussian Federation202413092024313226239Copyright © Полетаев А.Ю., Парамонов И.В., Колупаев Е.М., 20242024Полетаев А.Ю., Парамонов И.В., Колупаев Е.М.Poletaev A.Y., Paramonov I.V., Kolupaev E.M.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://www.mais-journal.ru/jour/article/view/1876

В работе сравнивается качество работы различных методов определения неявно упоминаемых аспектов социально-экономической жизни в публицистических предложениях на русском языке. Задача определения неявно упоминаемых аспектов является вспомогательной для задач аспектно-ориентированного анализа тональности. Эксперименты проводились на корпусе предложений, извлечённых из политической агитации. Лучшие результаты, с F1-мерой, достигающей 0.84, были получены с использованием эмбеддингов Navec и классификаторов, основанных на методе опорных векторов. Достаточно высокие результаты, с F1-мерой до 0.77, были получены при использовании модели «мешок слов» и наивного байесовского классификатора. Остальные методы показали более низкие результаты. Также в ходе экспериментов было выявлено, что качество определения различных аспектов может достаточно сильно отличаться. Лучше всего определяются аспекты, с которыми в речи связаны характерные слова-маркеры, например, «здравоохранение» и «проведение выборов» Хуже всего определяются упоминания достаточно общих аспектов, таких как «качество управления».

The paper compares performance of various methods of automatic implicit aspect detection in publicism sentences in Russian. The task of implicit aspect detection is an auxiliary task in the aspect-oriented sentiment analysis. The experiments were conducted on a corpus of sentences extracted from political campaign materials. The best results, with F1-measure reaching 0.84, were obtained using the Navec embeddings and classifiers based on the support vector machine method. Fairly high results, with F1-measure reaching 0.77, were obtained using the bag-of-words model and the naive Bayesian classifier. Other methods showed lower performance. It was also revealed during the experiments that the detection quality can differ significantly between the aspects. The detection quality is the highest for the aspects associated with characteristic marker words, for example, “health car” and “holding elections”. More general aspects, such as “quality of governance”, are detected with the worst quality.

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The authors declare that there are no conflicts of interest present.