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With the development of Web2.0, web mining applications pay more attention to blog pages. In order to prevent noises in blog pages from affecting the precision of web mining algorithms, it is very necessary to acquire posts from blog pages correctly. In this paper, the authors propose a blog post extraction algorithm which uses title finding. There are two stages in this algorithm. In the first stage, text nodes which indicate the title of the post are found and used as the beginning of the post. They take a machine learning approach to realize this stage, and employ SVM as classification model. In the second stage, they find the end of the post. Two methods are introduced in this stage, one uses VIPS segmentation results, and the other is based on hand-coded rules.
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