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Использование интеллектуального анализа текста для обнаружения скрытой информации в исторических источниках

descriptionPublicationkeyboard_double_arrow_right Article 01 Jan 2023 Russian Publisher:Российский государственный профессионально-педагогический университет
Authors: Kuznetsov, A. V.;

Использование интеллектуального анализа текста для обнаружения скрытой информации в исторических источниках

Abstract

В статье представлен обзор того, как интеллектуальный анализ текста используется для выявления скрытой информации в исторических текстах. Внимание акцентируется на методе тематического моделирования и моделях эмбеддингов слов. Статья иллюстрирует, как эти методы использовались в конкретных исторических исследованиях. Делается вывод о том, что интеллектуальный анализ текста является полезным инструментом для обнаружения скрытой информации в исторических текстах.

The article presents an overview of how text mining can be employed to reveal hidden information in historical texts. The attention is focused on the method of thematic modeling and word embedding models. The article illustrates how these techniques have been utilized in historical research. It concludes that text mining is a useful tool for uncovering hidden information in historical.

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Keywords

ТЕМАТИЧЕСКОЕ МОДЕЛИРОВАНИЕ, TEXT MINING, ИСТОРИЧЕСКИЕ ИСТОЧНИКИ, WORD EMBEDDING, ЭМБЕДДИНГИ СЛОВ, HISTORICAL SOURCES, ИНТЕЛЛЕКТУАЛЬНЫЙ АНАЛИЗ ТЕКСТ, TOPIC MODELING

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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