  This paper presents an approach to classify documents in any language into an
English topical label space, without any text categorization training data. The
approach, Cross-Lingual Dataless Document Classification (CLDDC) relies on
mapping the English labels or short category description into a Wikipedia-based
semantic representation, and on the use of the target language Wikipedia.
Consequently, performance could suffer when Wikipedia in the target language is
small. In this paper, we focus on languages with small Wikipedias,
(Small-Wikipedia languages, SWLs). We use a word-level dictionary to convert
documents in a SWL to a large-Wikipedia language (LWLs), and then perform CLDDC
based on the LWL's Wikipedia. This approach can be applied to thousands of
languages, which can be contrasted with machine translation, which is a
supervision heavy approach and can be done for about 100 languages. We also
develop a ranking algorithm that makes use of language similarity metrics to
automatically select a good LWL, and show that this significantly improves
classification of SWLs' documents, performing comparably to the best bridge
possible.
