7. A system for ranking documents, comprising: a processor that is adapted to execute stored instructions; and a system memory, wherein the system memory comprises code configured to: identify a plurality of query-title pairs based on clickthrough data for a plurality of documents, wherein the title in each query-title pair is a title of one of the documents of the plurality of documents; build a latent semantic model, a query being considered as expressed in a first language and the document being considered as expressed in a second language, the latent semantic model being based on the plurality of query-title pairs by building a bilingual topic model by learning a semantic representation of a query based on a likelihood that the query is a semantics-based translation of each of the plurality of documents; rank the plurality of documents for a search based on a distance between vector representations of a query and a title of each of the plurality of documents within a semantic space, wherein a projection matrix is used to map the vector representations of the query and the title of each of the plurality of documents to the semantic space, wherein the semantic space comprises a dense, low-dimensional space; and rank the plurality of documents for the search based on the latent semantic model.
10. The system of claim 7 , wherein the system memory comprises code configured to rank the plurality of documents for the search based on the likelihood that the query is the semantics-based translation of each of the plurality of documents.