论广告英语的语言特点

本探讨了广告英语的语义特点、词汇特点及句法特点。     

「Vテモラウ」的意义和用法——具有事先请求的情况

「Vテモラウ」的意义与其先行动词的意义和モラウ的意义这两个因素有关。本文首先综合先行研究的观点分析提取独立动词「モラウ」的语义特征,然后,从「Vテモラウ」的先行动词的特征和「モラウ」的语义特征两方面分析「Vテモラウ」的句义和用法,确定具有事先请求的情况的「Vテモラウ」句的意义下位分类存在请求义、指示义、表达愿望义、许可义。     

中医方剂领域本体建模和语义推理实现

针对目前中医领域知识共享的现状,结合对中医方剂领域知识特点的分析,采用骨架法构建基于OWL语言的方剂本体模型。并以中医诊断和开方为应用场景,提出基于SWRL规则结合Jess推理引擎的方法,尝试中医领域本体的语义推理。     

《医学信息检索与利用》课程PBL-LBL教学方法改革探讨

针对《医学信息检索与利用》这门课程在我校各医学专业教学的现状与问题,将PBL与LBL相结合的教学法引入教学中,既提高了教师的自身素质,又培养了学生的自主学习能力,形成教学相长的良好循环。     

基于知识元语义链接的知识网络构建

本文基于Brookes文献中的知识节点及Swanson文献间的隐性关联概念,提出了一种基于知识元本体语义链接的知识网络实现流程。文中给出了文献单元和Web信息的知识元认知抽取方法,知识元本体的结构建立和语义互联,以及利用Protégé技术实现基于知识元语义链接的知识网络构建步骤。     

情报检索语言的词汇控制问题

本文在情报检索语言和自然语言原理、交流方式等简要比较的基础上,对情报检索中词汇控制的必要性,词汇控制的内容,原则以及词汇控制对检索效率的影响,进行了探讨。     

光盘数据库检索方式的新发展

The main changes in the three commonly-used CD-ROM retrieval systems are introduced,including the enhancement of the window functions,the change of the field index function in SP software and the new saving and reusing functions of ProQuest for its retrieval strategies.     

语义网格环境下数字图书馆的服务整合

语义网络和网格技术在数字图书馆建设中的重要性已被业界所关注。语义网络应用于数字图书馆建设可以对其资源进行基于语义的标注,提供基于语义的资源浏览与检索;网格技术应用于数字图书馆建设可以整合分布、异构、自治的数字资源,获得资源透明调用的能力。     

Adaptive multi-feature fusion via cross-entropy normalization for effective image retrieval

Multi-feature fusion has achieved gratifying performance in image retrieval. However, some existing fusion mechanisms would unfortunately make the result worse than expected due to the domain and visual diversity of images. As a result, a burning problem for applying feature fusion mechanism is how to figure out and improve the complementarity of multi-level heterogeneous features. To this end, this paper proposes an adaptive multi-feature fusion method via cross-entropy normalization for effective image retrieval. First, various low-level features (e.g., SIFT) and high-level semantic features based on deep learning are extracted. Under each level of feature representation, the initial similarity scores of the query image w.r.t. the target dataset are calculated. Second, we use an independent reference dataset to approximate the tail of the attained initial similarity score ranking curve by cross-entropy normalization. Then the area under the ranking curve is calculated as the indicator of the merit of corresponding feature (i.e., a smaller area indicates a more suitable feature.). Finally, fusion weights of each feature are assigned adaptively by the statistically elaborated areas. Extensive experiments on three public benchmark datasets have demonstrated that the proposed method can achieve superior performance compared with the existing methods, improving the metrics mAP by relatively 1.04% (for Holidays), 1.22% (for Oxf5k) and the N-S by relatively 0.04 (for UKbench), respectively.