| Abstract: | Webpages are mainly distinguished by their topic (e.g., politics, sports etc.) and genre (e.g.,
blogs, homepages, e-shops, etc.). Automatic detection of webpage genre could considerably
enhance the ability of modern search engines to focus on the requirements of the userﾒs
information need. In this paper, we present an approach to webpage genre detection based
on a fully-automated extraction of the feature set that represents the style of webpages.
The features we propose (character n-grams of variable length and HTML tags) are language-
independent and easily-extracted while they can be adapted to the properties of
the still evolving web genres and the noisy environment of the web. Experiments based
on two publicly-available corpora show that the performance of the proposed approach
is superior in comparison to previously reported results. It is also shown that character
n-grams are better features than words when the dimensionality increases while the binary
representation is more effective than the term-frequency representation for both feature
types. Moreover, we perform a series of cross-check experiments (e.g., training using a
genre palette and testing using a different genre palette as well as using the features
extracted from one corpus to discriminate the genres of the other corpus) to illustrate
the robustness of our approach and its ability to capture the general stylistic properties
of genre categories even when the feature set is not optimized for the given corpus. |
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Greek