论文标题
基于深度语言模型的跨媒体科学研究成就检索
Cross-Media Scientific Research Achievements Retrieval Based on Deep Language Model
论文作者
论文摘要
科学和技术的大数据包含许多跨媒体信息。科学论文中有图像和文本。SINGLE模态搜索方法不能很好地满足科学研究人员的需求。本文提出了一种基于深层语言模型（CardL）的跨密歇根科学研究成就检索方法。科学研究成就，然后通过不同模态数据之间的语义相似性来实现跨媒体检索。实验结果表明，所提出的CARDL方法比现有方法获得了更好的跨模式检索性能。关键词科学和技术大数据；跨媒体检索；跨媒体语义协会学习；深层语言模型；语义相似性
Science and technology big data contain a lot of cross-media information.There are images and texts in the scientific paper.The s ingle modal search method cannot well meet the needs of scientific researchers.This paper proposes a cross-media scientific research achievements retrieval method based on deep language model (CARDL).It achieves a unified cross-media semantic representation by learning the semantic association between different modal data, and is applied to the generation of text semantic vector of scientific research achievements, and then cross-media retrieval is realized through semantic similarity matching between different modal data.Experimental results show that the proposed CARDL method achieves better cross-modal retrieval performance than existing methods. Key words science and technology big data ; cross-media retrieval; cross-media semantic association learning; deep language model; semantic similarity
