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Bisociative Literature-Based Discovery: Lessons Learned and New Word Embedding Approach
New Generation Computing ( IF 2.6 ) Pub Date : 2020-10-06 , DOI: 10.1007/s00354-020-00108-w
Nada Lavrač , Matej Martinc , Senja Pollak , Maruša Pompe Novak , Bojan Cestnik

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.

中文翻译:

基于双联文学的发现:经验教训和新词嵌入方法

基于双关联文献的发现领域旨在挖掘科学文献,以揭示不同专业领域之间尚未发现的联系。本文概述了几种基于异常值的文献挖掘方法来桥接术语检测,以及从选定的基于生物医学文献的发现应用中吸取的经验教训。该论文还探讨了基于双关联文献发现的新前景,提出了一种用于跨域文献挖掘的基于嵌入的高级技术。
更新日期:2020-10-06
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