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Learning Scientific Document Representations via Triple-Source Automatic Supervision Without Annotations or Citations

Learning meaningful representations of scientific documents is essential for information retrieval, knowledge discovery, and recommendation systems. Traditional methods such as TF-IDF rely on lexical matching and fail to capture deeper semantic relationships, while transformer-based approaches typic...

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Principais autores: Mussa Turdalyuly, Ainur Tursynkhan, Aigerim Yerimbetova, Tolganay Turdalykyzy, Bakzhan Sakenov, Nurzhan Mukazhanov, Nazerke Baisholan
Formato: Artigo
Idioma:Inglês
Publicado em: MDPI AG 2026-04-01
coleção:Computers
Assuntos:
semantic search
document retrieval
scientific document embeddings
contrastive learning
information retrieval
scientific literature analysis
Acesso em linha:https://www.mdpi.com/2073-431X/15/5/268
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