Automatic key concept identification from text is the main challenging task in information extraction, information retrieval, digital libraries, ontology learning, and text analysis. The main difficulty lies in the issues with the text data itself, such as noise in text, diversity, scale of data, context dependency and word sense ambiguity. To cope with this challenge, numerous supervised and unsupervised approaches have been devised. The existing topical clustering-based approaches for keyphrase extraction are domain dependent and overlooks semantic similarity between candidate features while extracting the topical phrases. In this paper, a semantic based unsupervised approach (KP-Rank) is proposed for keyphrase extraction. In the proposed approach, we exploited Latent Semantic Analysis (LSA) and clustering techniques and a novel frequency-based algorithm for candidate ranking is introduced which considers locality-based sentence, paragraph and section frequencies. To evaluate the performance of the proposed method, three benchmark datasets (i.e. Inspec, 500N-KPCrowed and SemEval-2010) from different domains are used. The experimental results show that overall, the KP-Rank achieved significant improvements over the existing approaches on the selected performance measures.
针对近场源参数估计计算复杂度大的问题,提出了一种基于对称阵列结构的快速估计算法。首先通过对称阵列结构构造多项式,通过求解多项式的根得到近场源的角度信息;在距离估计的时候,结合压缩多重信号分类算法(Compressed multiple signal classification,C-MUSIC)的思想,将菲涅尔区域分为若干个子区域,通过构造噪声子空间簇的交集,得到新的谱函数,将原来整个区域搜索变换成小区域搜索,可节省运算时间。通过仿真试验验证了算法的有效性,证明该算法的运算复杂度与传统估计算法相比得到了很大改善。 相似文献
