document: Learning block importance models for web pages Previous work shows that a web page can be partitioned into multiple segments or blocks , and often the importance of those blocks in a page is not equivalent . Also , it has been proven that differentiating noisy or unimportant blocks from pages can facilitate web mining , search and accessibility . However , no uniform approach and model has been presented to measure the importance of different segments in web pages . Through a user study , we found that people do have a consistent view about the importance of blocks in web pages . In this paper , we investigate how to find a model to automatically assign importance values to blocks in a web page . We define the block importance estimation as a learning problem . First , we use a vision-based page segmentation algorithm to partition a web page into semantic blocks with a hierarchical structure . Then spatial features ( such as position and size ) and content features ( such as the number of images and links ) are extracted to construct a feature vector for each block . Based on these features , learning algorithms are used to train a model to assign importance to different segments in the web page . In our experiments , the best model can achieve the performance with Micro-F1 79 % and Micro-Accuracy 85.9 % , which is quite close to a person 's view .
doc_bio_tags: O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O