Blogosonomy Autotagging Any Text Using Bloggers' Knowledge

• Published in: Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence pp. 205 - 212
• Main Authors: Fujimura, Shigeru, Fujimura, KO, Okuda, Hidenori
• Format: Conference Proceeding
• Language: English
• Published: Washington, DC, USA IEEE Computer Society 02.11.2007
• Series: ACM Conferences
• Subjects: Applied computing > Document management and text processing > Document management; Computing methodologies > Machine learning > Learning paradigms > Supervised learning > Supervised learning by classification; Computing methodologies > Machine learning > Learning paradigms > Unsupervised learning > Cluster analysis; Computing methodologies > Machine learning > Machine learning approaches > Classification and regression trees; Information systems > Information retrieval > Document representation; Information systems > Information retrieval > Evaluation of retrieval results
• ISBN: 0769530265; 9780769530260
• DOI: 10.1109/WI.2007.31

There are at least three barriers to utilizing blog tags in classification or navigation: 40% of entries are not (from our observations) tagged, there are many orthographic or synonymous tag variations, and not all tags are informative.We propose a method of multi-autotagging, based on k-NN, which is a case-based classijication method. Our method also has the functions of merging tags with the same meaning and identifying informative tags. For realizing these functions, we propose the term weighting method named residual document frequency(RDF); it can score the similarity between tags. Experiments show the effectiveness of our methods. Our autotagging system is generic and can assign tag(s) to any text as well as blog entries although the training data is collected from the blogosophere.