Learning to recognize webpage genres
Information Processing and Management: an International Journal
Classifying Web Pages by Genre: An n-Gram Approach
WI-IAT '09 Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
A solution to the exact match on rare item searches: introducing the lost sheep algorithm
Proceedings of the International Conference on Web Intelligence, Mining and Semantics
Open-Set classification for automated genre identification
ECIR'13 Proceedings of the 35th European conference on Advances in Information Retrieval
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The research reported in this paper is the first phase of a larger project on the automatic classification of web pages by their genres, using n-gram representations of the web pages. In this study, the textual content of web pages is used to create feature sets consisting of the most frequent n-grams and their associated frequencies. We present three methods, each of which uses a distance measure to determine the dissimilarity between two feature sets. Each method forms a feature set for every web page in the test set, however the formation of feature sets from the training set differs between methods: we experiment using one feature set per web page, per genre, and a combination of genre-based feature sets supplemented by subgenre feature sets. We present results for a balanced corpus of seven genres (blog, eshop, FAQs, front page, listing, home page, and search page). Initial results are encouraging.