  Many applications require categorization of text documents using predefined
categories. The main approach to performing text categorization is learning
from labeled examples. For many tasks, it may be difficult to find examples in
one language but easy in others. The problem of learning from examples in one
or more languages and classifying (categorizing) in another is called
cross-lingual learning. In this work, we present a novel approach that solves
the general cross-lingual text categorization problem. Our method generates,
for each training document, a set of language-independent features. Using these
features for training yields a language-independent classifier. At the
classification stage, we generate language-independent features for the
unlabeled document, and apply the classifier on the new representation.
  To build the feature generator, we utilize a hierarchical
language-independent ontology, where each concept has a set of support
documents for each language involved. In the preprocessing stage, we use the
support documents to build a set of language-independent feature generators,
one for each language. The collection of these generators is used to map any
document into the language-independent feature space.
  Our methodology works on the most general cross-lingual text categorization
problems, being able to learn from any mix of languages and classify documents
in any other language. We also present a method for exploiting the hierarchical
structure of the ontology to create virtual supporting documents for languages
that do not have them. We tested our method, using Wikipedia as our ontology,
on the most commonly used test collections in cross-lingual text
categorization, and found that it outperforms existing methods.
