No abstract

Core–shell imprinting microspheres for the selective and rapid recognition of thymopentin with the aid of a novel polymeric ionic liquid.

Existing works for extracting navigation objects from webpages focus on navigation menus, so as to reveal the information architecture of the site. However, web 2.0 sites such as social networks, e-commerce portals etc. are making the understanding of the content structure in a web site increasingly di cult. Dynamic and personalized elements such as top stories, recommended list in a webpage are vital to the understanding of the dynamic nature of web 2.0 sites. To be er understand the content structure in web 2.0 sites, in this paper we propose a new extraction method for navigation objects in a webpage. Our method will extract not only the static navigation menus, but also the dynamic and personalized page-speci c navigation lists. Since the navigation objects in a webpage naturally come in blocks, we rst cluster hyperlinks into di erent blocks by exploiting spatial locations of hyperlinks, the hierarchical structure of the DOM-tree and the hyperlink density.en we identify navigation objects from those blocks using the SVM classi er with novel features such as anchor text lengths etc. Experiments on real-world data sets with webpages from various domains and styles veri ed the e ectiveness of our method.