With the increase of the number of web pages, it is very difficult to find wanted information easily and quickly out of thousands of web pages retrieved by a search engine. To solve this problem, many researches propose to classify documents according to their genre, which is another criteria to classify documents different from the topic. Most of these works assign a document to only one genre. In this paper we propose a new flexible approach for document genre categorization. Flexibility means that our approach assigns a document to all predefined genres with different weights. The proposed approach is based on the combination of two homogenous classifiers: contextual and structural classifiers. The contextual classifier uses the URL, while the structural classifier uses the document structure. Both contextual and structural classifiers are centroid-based classifiers. Experimentations provide a micro-averaged break-even point (BEP) more than 85%, which is better than those obtained by other categorization approaches.

A wish is “a desire or hope for something to happen. ” In December 2007, people from around the world offered up their wishes to be printed on confetti and dropped from the sky during the famous New Year’s Eve “ball drop ” in New York City’s Times Square. We present an in-depth analysis of this collection of wishes. We then leverage this unique resource to conduct the first study on building general “wish detectors ” for natural language text. Wish detection complements traditional sentiment analysis and is valuable for collecting business intelligence and insights into the world’s wants and desires. We demonstrate