document: Mining comparable bilingual text corpora for cross-language information integration Integrating information in multiple natural languages is a challenging task that often requires manually created linguistic resources such as a bilingual dictionary or examples of direct translations of text . In this paper , we propose a general cross-lingual text mining method that does not rely on any of these resources , but can exploit comparable bilingual text corpora to discover mappings between words and documents in different languages . Comparable text corpora are collections of text documents in different languages that are about similar topics ; such text corpora are often naturally available ( e.g. , news articles in different languages published in the same time period ) . The main idea of our method is to exploit frequency correlations of words in different languages in the comparable corpora and discover mappings between words in different languages . Such mappings can then be used to further discover mappings between documents in different languages , achieving cross-lingual information integration . Evaluation of the proposed method on a 120MB Chinese-English comparable news collection shows that the proposed method is effective for mapping words and documents in English and Chinese . Since our method only relies on naturally available comparable corpora , it is generally applicable to any language pairs as long as we have comparable corpora .
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