Building a language-independent keyword-based system with the Wikipedia Miner by Gauthier Lemoine.
From the post:
Extracting keywords from texts and HTML pages is a common subject that opens doors to a lot of potential applications. These include classification (what is this page topic?), recommendation systems (identifying user likes to recommend the more accurate content), search engines (what is this page about?), document clustering (how can I pack different texts into a common group) and much more.
Most applications of these are usually based on only one language, usually english. However, it would be better to be able to process document in any language. For example, a case in a recommender system would be a user that speaks French and English. In his history, he gave positive ratings to a few pages containing the keyword “Airplane”. So, for next recommendations, we would boost this keyword. With a language-independent approach, we would also be able to boost pages containing “Avion”, the french term for airplane. If the user gave positive ratings to pages in English containing “Airplane”, and in French containing “Avion”, we would also be able to merge easily into the same keyword to build a language-independent user profile that will be used for accurate French and English recommendations.
This articles shows one way to achieve good results using an easy strategy. It is obvious that we can achieve better results using more complex algorithms.
The NSA can hire translators so I would not bother sharing this technique for harnessing the thousands of expert hours in Wikipedia with them.
Bear in mind that Wikipedia does not reach a large number of minority languages, dialects, and certainly not deliberate obscurity in any language. Your mileage will vary depending upon your particular use case.