Abstract
Webpage analysis is carried out for various purposes such as webpage segmentation. The goal of webpage segmentation is to divide a page into blocks that have similar elements. A fusion approach that combines different analyses is required in order to obtain high segmentation accuracy. In this paper, we propose a new fusion model for webpage segmentation, where we (1) merge webpage content into basic-blocks by simulating human perception; and, (2) identify similar blocks using semantic text similarity and regroup these similar blocks as fusion blocks. This approach is applied to three public datasets and evaluated by comparing with state-of-the-art algorithms. The results characterize that our proposed approach outperforms other existing webpage segmentation methods, in terms of accuracy.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.