A Segment-based Weighting Technique for URL-based Genre Classification of Web Pages

A Segment-based Weighting Technique for URL-based Genre Classification of Web Pages

Semi-supervised Graph-based Genre Classification for Web Pages

Until now, it is still unclear which set of features produces the best result in automatic genre classification on the web. Therefore, in the first set of experiments, we compared a wide range of contentbased features which are extracted from the data appearing within the web pages. The results show that lexical features such as word unigrams and character n-grams have more discriminative power...

Genre Classification of Web Pages

Genre classification means to discriminate between documents by means of their form, their style, or their targeted audience. Put another way, genre classification is orthogonal to a classification based on the documents’ contents. While most of the existing investigations of an automated genre classification are based on news articles corpora, the idea here is applied to arbitrary Web pages. W...

Training a Genre Classifier for Automatic Classification of Web Pages

This paper presents experimentson classifyingweb pages by genre. Firstly, a corpus of 1 539 manually labeled web pages was prepared. Secondly, 502 genre features were selected based on the literature and the observation of the corpus. Thirdly, these features were extracted from the corpus to obtain a data set. Finally, two machine learning algorithms, one for induction of decision trees (J48) a...

A Combination based on OWA Operators for Multi-label Genre Classification of web pages

This paper presents a new method for genre identification that combines homogeneous classifiers using OWA (Ordered Weighted Averaging) operators. Our method uses character n-grams extracted from different information sources such as URL, title, headings and anchors. To deal with the complexity of web pages, we applied MLKNN as a multi-label classifier, in which a web page can be affected by mor...