9. A method comprising: under a control of one or more processors, identifying multiple concept-units from a multi-language document corpus, a respective concept-unit including a set of documents in a plurality of languages describing a particular concept, the identifying including identifying one or more hyperlinks or references within a respective document that identify one or more other documents in one or more other languages relating to the particular concept; maintaining a separation of term-by-document matrices for the plurality of languages; and inferring a plurality of universal topics from the multiple concept-units, at least one respective universal topic being defined by a plurality of topic word distributions in the plurality of languages, at least one of the plurality of topic word distributions for the respective universal topic corresponding to a respective language from the plurality of languages and including one or more words in the respective language with corresponding probability values characterizing the respective universal topic.
9. A method comprising: under a control of one or more processors, identifying multiple concept-units from a multi-language document corpus, a respective concept-unit including a set of documents in a plurality of languages describing a particular concept, the identifying including identifying one or more hyperlinks or references within a respective document that identify one or more other documents in one or more other languages relating to the particular concept; maintaining a separation of term-by-document matrices for the plurality of languages; and inferring a plurality of universal topics from the multiple concept-units, at least one respective universal topic being defined by a plurality of topic word distributions in the plurality of languages, at least one of the plurality of topic word distributions for the respective universal topic corresponding to a respective language from the plurality of languages and including one or more words in the respective language with corresponding probability values characterizing the respective universal topic. 10. A method as recited in claim 9 , wherein the inferring comprises performing a latent Dirichlet allocation analysis.