a entity relation extraction method based on wikipedia and pattern clustering

该文提出了一种基于维基百科和模式聚类的方法,旨在从开放文本中抽取高准确率的中文关系实体对.首次使用从人工标注知识体系知网到维基百科实体映射的方式获取关系实例,并且充分利用了维基百科的结构化特性,该方法很好地解决了实体识别的问题,生成了准确而显著的句子实例；进一步,提出了显著性假设和关键词假设,在此基础上构建基于关键词的分类及层次聚类算法,显著提升了模式的可信度.实验结果表明该方法有效提升了句子实例及模式的质量,获得了良好的抽取性能

基于Contourlet变换的压缩感知MRI

Reducing the acquisition time is important for clinical magnetic resonance imaging (MRI). Compressed sensing has recently emerged as a theoretical foundation for the reconstruction of magnetic resonance images from undersampled k-space measurements, assuming those images are sparse in a certain transform domain. However, most real-world signals are compressible rather than exactly sparse. For example, the commonly used two-dimensional wavelet for compressed sensing MRI (CS-MRI) does not sparsely represent curves and edges. In this article, we introduce a geometric image transform, the contourlet, to overcome this shortage. In addition, the improved redundancy provided by the contourlet can successfully suppress the pseudo-Gibbs phenomenon, a tiresome artefact produced by undersampling of k-space, around the singularities of images. For numerical calculation, a simple but effective iterative thresholding algorithm is employed to solve l1 norm optimization for CS-MRI. Considering the recovered information and image features, we introduce three objective criteria, which are the peak signal-to-noise ratio (PSNR), mutual information and transferred edge information, to evaluate the performance of different image transforms. Simulation results demonstrate that contourlet-based CS-MRI can better reconstruct the curves and edges than traditional wavelet-based methods, especially at low k-space sampling rateThis work was partially supported by NNSF of China under Grants (10774125, 10875101, and 10605019). Xiaobo Qu and Di Guo would like to acknowledge the fellowship of Postgraduates Overseas Study Program for Building High-Level Universities from the Chinese Scholarship Council