Extracting Content Structure for Web Pages Based on Visual Representation

By Deng Cai, Shipeng Yu, Ji-rong Wen, Wei-Ying Ma. Asia-Pacific Web Conference, 2003

425 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Information Systems

View Paper: https://www.semanticscholar.org/paper/84372852886ddcb49e9adf9c5facf53e1bde9696

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

1. Xiaofeng Meng
2. Wolfgang Nejdl
3. Weiyi Meng
4. Wei David Liu
5. Xiangnan He
6. Tat-seng Chua

In Information Systems

1. Xiaofeng Meng
2. Péter Fankhauser
3. Debasis Ganguly
4. Wolfgang Nejdl
5. Mandar Mitra
6. Christian Kohlschütter
7. Weiyi Meng
8. Dwaipayan Roy

Citations in the corpus

• Recommender Systems (2016, neutral): “The use of visual representations for extracting content structure from Web pages is described in [126].”
• ViDE: A Vision-Based Approach for Deep Web Data Extraction (2010, methodology): “VIPS algorithm [4] to represent Web pages, but the tag information is still an important feature in HCRF.”
• Boilerplate detection using shallow text features (2010, methodology): “Template detection is strongly related to the more generic problem of web page segmentation, which has been addressed at DOM-level [7], by exploiting term entropies [17] or by using Vision-based features [5].”
• To Clean or Not to Clean (2018, background): “Of course, parsing of Web pages and extraction of useful content, particularly in the presence of navigational and advertisement blocks, and syntactic errors, is a challenging task that has been extensively studied [11, 38, 56, 68, 69].”