1. A method comprising: identifying a concept-unit from a multi-language document corpus, the concept-unit including a set of documents in different languages describing a particular concept; modeling the concept-unit identified from the multi-language document corpus to create a generative model, wherein the generative model represents at least: (a) a plurality of universal topics, individual ones of the plurality of universal topics being defined by a plurality of topic word distributions corresponding respectively to the different languages; and (b) a universal topic distribution associated with the concept-unit, the universal topic distribution identifying: (i) two or more universal topics for which documents describing the concept unit are to contain; and (ii) a relative significance of individual universal topics of the two or more universal topics within the concept unit; and classifying a set of documents of an unclassified document corpus using the generative model, the classifying comprising: obtaining the universal topic distribution for the set of documents of the generative model, obtaining a topic distribution of the set of documents of the unclassified document corpus, comparing the universal topic distribution to the topic distribution of the set of documents of the unclassified document corpus; and based on the comparing, classifying one or more documents of the set of documents of the unclassified document corpus.
1. A method comprising: identifying a concept-unit from a multi-language document corpus, the concept-unit including a set of documents in different languages describing a particular concept; modeling the concept-unit identified from the multi-language document corpus to create a generative model, wherein the generative model represents at least: (a) a plurality of universal topics, individual ones of the plurality of universal topics being defined by a plurality of topic word distributions corresponding respectively to the different languages; and (b) a universal topic distribution associated with the concept-unit, the universal topic distribution identifying: (i) two or more universal topics for which documents describing the concept unit are to contain; and (ii) a relative significance of individual universal topics of the two or more universal topics within the concept unit; and classifying a set of documents of an unclassified document corpus using the generative model, the classifying comprising: obtaining the universal topic distribution for the set of documents of the generative model, obtaining a topic distribution of the set of documents of the unclassified document corpus, comparing the universal topic distribution to the topic distribution of the set of documents of the unclassified document corpus; and based on the comparing, classifying one or more documents of the set of documents of the unclassified document corpus. 4. A method as recited in claim 1 , wherein the identifying the concept-unit comprises identifying hyperlinks within the set of documents that identify other documents in other languages relating to common concepts.