Automatic Extraction of Blog Post from Diverse Blog Pages

• Published in: 2012 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology Vol. 1; pp. 129 - 136
• Main Authors: Chia-Hui Chang, Jhih-Ming Chen
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.12.2012
• Subjects: blog post extraction; maximum sequence; sequence labeling
• ISBN: 9781467360579; 1467360570
• DOI: 10.1109/WI-IAT.2012.25

Blog post extraction is essential for researches on blogosphere. In this paper, we address the issue of extracting blog posts from diverse blog pages, which aims at automatically and precisely finding the location of each blog post. Most of the previous researches focused on extracting main content from news pages, but the problem becomes more complex when one turns to blog pages. Our research is based on the combination of maximum scoring subsequence [11] and text-to-tag ratio [18] to develop algorithms that are suitable for blog pages. The first method that we propose is PTR Scoring, which combines postto-tag ratio with maximum scoring subsequence. The second method is CRF Scoring, which applies Conditional Random Field to train a sequence labeling model and use maximum scoring subsequence to improve the accuracy of extraction. The experimental results show that CRF Scoring achieves the best F-Measure at 91.9% compared with other methods.