As the amount of web page increases, searching for semi-structured documents is gaining greater attention. The traditional approach for extracting data from web page documents is to write specialized programs, called wrappers that identify data of interest and map them to some suitable format. However, developing wrappers manually has many well known shortcomings, mainly due to the difficulty in writing and maintaining them for continually changing web data. Moreover, there is no one wrapper program that can treat all kinds of web pages. In this paper, we aim to extract relevant and meaningful snippets from as many web pages as possible, using the shallow feature of HTML documents to discover and analyze the relevant components. Also, we introduced a new feature called GAP and verified the effectiveness of GAP by conducting a SVM learning experiment.
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