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Title: Bisociative literature-based discovery : lessons learned and new word embedding approach Authors: ID Lavrač, Nada (Author) ID Martinc, Matej (Author) ID Pollak, Senja (Author) ID Pompe Novak, Maruša (Author) ID Cestnik, Bojan (Author) Files: URL - Source URL, visit http://dx.doi.org/10.1007/s00354-020-00108-w   PDF - Presentation file, download (1,54 MB) MD5: D27E500857A776CE4BEB355228C19770   Language: English Typology: 1.01 - Original Scientific Article Organization: NIB - National Institute of Biology Abstract: The field of bisociative literature-based discovery aims at mining scientific literature to reveal yet uncovered connections between different fields of specialization. This paper outlines several outlier-based literature mining approaches to bridging term detection and the lessons learned from selected biomedical literature-based discovery applications. The paper addresses also new prospects in bisociative literature-based discovery, proposing an advanced embeddings-based technology for cross-domain literature mining. Publication status: Published Publication version: Version of Record Publication date: 06.10.2020 Year of publishing: 2020 Number of pages: str. 773-800 Numbering: Vol. 38 PID: 20.500.12556/DiRROS-19534  UDC: 004.8 ISSN on article: 0288-3635 DOI: 10.1007/s00354-020-00108-w  COBISS.SI-ID: 31844867  Publication date in DiRROS: 22.07.2024 Views: 1 Downloads: 2 Metadata:     Cite this work Plain text BibTeX EndNote XML EndNote/Refer RIS ABNT ACM Ref AMA APA Chicago 17th Author-Date Harvard IEEE ISO 690 MLA Vancouver : Copy citation

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Record is a part of a journal

Title:	New generation computing
Shortened title:	New gener. comput.
Publisher:	Ohmsha, Springer-Verlag
ISSN:	0288-3635
COBISS.SI-ID:	26018560

Document is financed by a project

Funder:	ARIS - Slovenian Research and Innovation Agency
Project number:	P2-0103-2015
Name:	Tehnologije znanja

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Funder:	ARIS - Slovenian Research and Innovation Agency
Project number:	J6-9372-2018
Name:	TermFrame: Terminologija in sheme znanja v medjezikovnem prostoru

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Funder:	EC - European Commission
Project number:	825153
Name:	Cross-Lingual Embeddings for Less-Represented Languages in European News Media
Acronym:	EMBEDDIA

Licences

License:	CC BY 4.0, Creative Commons Attribution 4.0 International
Link:	http://creativecommons.org/licenses/by/4.0/
Description:	This is the standard Creative Commons license that gives others maximum freedom to do what they want with the work as long as they credit the author.

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