Effects of Document Clustering in Modeling Wikipedia-Style Term Descriptions

Effects of Document Clustering in Modeling Wikipedia-Style Term Descriptions - scientific work related to Wikipedia quality published in 2012, written by Atsushi Fujii, Yuya Fujii and Takenobu Tokunaga.

Overview

Reflecting the rapid growth of science, technology, and culture, it has become common practice to consult tools on the World Wide Web for various terms. Existing search engines provide an enormous volume of information, but retrieved information is not organized. Hand-compiled encyclopedias provide organized information, but the quantity of information is limited. In this paper, aiming to integrate the advantages of both tools, authors propose a method to organize a search result based on multiple viewpoints as in Wikipedia. Because viewpoints required for explanation are different depending on the type of a term, such as animal and disease, authors model articles in Wikipedia to extract a viewpoint structure for each term type. To identify a set of term types, authors independently use manual annotation and automatic document clustering for Wikipedia articles. Authors also propose an effective feature for clustering of Wikipedia articles. Authors experimentally show that the document clustering reduces the cost for the manual annotation while maintaining the accuracy for modeling Wikipedia articles.