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Небесная энциклопедия

Космические корабли и станции, автоматические КА и методы их проектирования, бортовые комплексы управления, системы и средства жизнеобеспечения, особенности технологии производства ракетно-космических систем

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Мониторинг СМИ и социальных сетей. Сканирование интернета, новостных сайтов, специализированных контентных площадок на базе мессенджеров. Гибкие настройки фильтров и первоначальных источников.

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Применить Всего найдено 2. Отображено 2.
28-07-2023 дата публикации

Bio-medical text overlapping relation joint extraction method and related equipment

Номер: CN116501830A
Автор: CHEN XIANLAI, SUN YU, CHEN XIANG, AN YING, HUANG JINCAI
Принадлежит:

The invention provides an overlapping relation joint extraction method and related equipment for biomedical texts, and the method comprises the steps: constructing an overlapping relation triple joint extraction framework with a fusion module, a subject extraction module and an object extraction module through obtaining to-be-extracted biomedical text data, inputting to-be-extracted biomedical text data into the overlapping relation triple joint extraction framework for extraction to obtain an overlapping relation triple of the biomedical text data; compared with the prior art, each text in the biomedical text data to be extracted is fused with a plurality of relation types through the fusion module; the information of the starting position of the main body is introduced when the ending position of the main body is extracted to help to extract the main body; meanwhile, when the object is extracted, the information of the starting position of the object and the relative distance information ...

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Номер записи: 1
23-06-2023 дата публикации

Text classification method and device based on hierarchical text graph structure learning and medium

Номер: CN116304061A
Автор: LONG JUN, WANG ZIDONG, YANG LIU, CHEN TINGXUAN, HUANG JINCAI
Принадлежит:

The invention discloses a text classification method and device based on hierarchical text graph structure learning and a medium, and the method comprises the following steps: S1, carrying out the preprocessing of a training set text according to three linguistic features, and obtaining three graph structure matrixes; s2, carrying out edge level graph structure learning to obtain three edge vectors; s3, redundancy is removed, and three text edge vectors are obtained; s4, performing weighted summation to obtain a text graph structure representation; s5, processing by adopting a graph convolutional neural network, and generating a graph-level text representation through a graph pooling layer; and S6, softmax classification is carried out, and the category with the maximum probability is a final classification result. The method has the advantages that the training set text is preprocessed by adopting three linguistic features, and a text classification problem is converted into a graph classification ...

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Номер записи: 2