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. 7. A method as recited in claim 1 , further comprising generating a recommendation using the generative model, the generating the recommendation comprising: obtaining a topic distribution of a reference document of a first language; obtaining a topic distributions of a plurality of documents of a second language; comparing the topic distributions between the reference document of the first language and the plurality of documents of the second language to identify topics of the documents; and recommending at least a first document of the second language that is related to the reference document based on its identified topics.