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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Autors principals:	Mussa Turdalyuly, Ainur Tursynkhan, Aigerim Yerimbetova, Tolganay Turdalykyzy, Bakzhan Sakenov, Nurzhan Mukazhanov, Nazerke Baisholan
Format:	Artigo
Idioma:	Inglês
Publicat:	MDPI AG 2026-04-01
Col·lecció:	Computers
Matèries:	semantic search document retrieval scientific document embeddings contrastive learning information retrieval scientific literature analysis
Accés en línia:	https://www.mdpi.com/2073-431X/15/5/268
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