An EM Based Training Algorithm for Cross-Language Text Categorization

Due to the globalization on the Web, many companies and institutions need to efficiently organize and search repositories containing multilingual documents. The management of these heterogeneous text collections increases the costs significantly because experts of different languages are required to organize these collections. CrossLanguage Text Categorization can provide techniques to extend existing automatic classification systems in one language to new languages without requiring additional intervention of human experts. In this paper we propose a learning algorithm based on the EM scheme which can be used to train text classifiers in a multilingual environment. In particular, in the proposed approach, we assume that a predefined category set and a collection of labeled train

• Type: Conference
• Year: 2005
• Where: WEBI
• Authors: Leonardo Rigutini, Marco Maggini, Bing Liu
• Keywords: CrossLanguage Text Categorization, Heterogeneous Text Collections, Internet Technology, Multilingual Documents, WEBI 2005