Cross Language Text Categorization by Acquiring Multilingual Domain Models from Comparable Corpora

In a multilingual scenario, the classical monolingual text categorization problem can be reformulated as a cross language TC task, in which we have to cope with two or more languages (e.g. English and Italian). In this setting, the system is trained using labeled examples in a source language (e.g. English), and it classifies documents in a different target language (e.g. Italian). In this paper we propose a novel approach to solve the cross language text categorization problem based on acquiring Multilingual Domain Models from comparable corpora in a totally unsupervised way and without using any external knowledge source (e.g. bilingual dictionaries). These Multilingual Domain Models are exploited to define a generalized similarity function (i.e. a kernel function) among documents in different languages, which is used inside a Support Vector Machines classification framework. The results show that our approach is a feasible and cheap solution that largely outperforms a baseline.