From Electronic Publication of a Medieval Manuscript to Big Data, or What Artificial Intelligence Knows about the Beginning of Slavic Books

IF 0.2 0 HUMANITIES, MULTIDISCIPLINARY Palaeobulgarica-Starobalgaristika Pub Date : 2023-10-23 DOI:10.59076/2603-2899.2023.3.10
Victor Baranov
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Abstract

The article describes the preparation of machine-readable linguistic resources based on medieval Slavic written monuments, as well as their use in systems for automated and automatic processing of large text data. The history of this area of applied Paleoslavistics is briefly shown on the example of several projects for the creation of electronic publications, collections and corpora of Slavic manuscripts. Particular attention is paid to the stages of development and the material of the Manuscript historical corpus (mansucripts.ru), which contains marked-up transliterations of Glagolitic and transcriptions of Cyrillic manuscripts of the 10th–15th centuries, as well as specialized tools for processing, demonstrating and analyzing non-standard graphic and spelling features and structure of texts. The labor-intensive and complex process of preparing copies of manuscripts and marking them up, unfortunately, is still the only way to convert a graphic image into a machine-readable form. It is noted that the tagged collections created on the basis of Slavic manuscripts make it possible to use the latter both for creating models for recognizing manuscripts in existing HTR systems and for developing new specialized tools for recognizing and analyzing Slavic manuscript heritage.
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从中世纪手稿的电子出版到大数据,或者人工智能对斯拉夫书籍起源的了解
本文描述了基于中世纪斯拉夫文字纪念碑的机器可读语言资源的准备,以及它们在自动和自动处理大型文本数据的系统中的使用。应用古斯拉夫学的这一领域的历史简要地展示了几个项目的例子,为创建电子出版物,收集和语料库的斯拉夫手稿。特别关注手稿历史语料库(mansuscripts .ru)的发展阶段和材料,其中包含10 - 15世纪格拉哥利文和西里尔文手稿的标记音译,以及用于处理,演示和分析非标准图形和拼写特征和文本结构的专门工具。不幸的是,准备手稿副本并对其进行标记这一劳动密集型和复杂的过程仍然是将图形图像转换为机器可读形式的唯一途径。值得注意的是,在斯拉夫手稿的基础上创建的有标签的收藏使得有可能使用后者来创建识别现有HTR系统中的手稿的模型,并开发新的专门工具来识别和分析斯拉夫手稿遗产。
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来源期刊
Palaeobulgarica-Starobalgaristika
Palaeobulgarica-Starobalgaristika HUMANITIES, MULTIDISCIPLINARY-
CiteScore
0.50
自引率
75.00%
发文量
0
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