Creating a language-independent meaning representation would benefit many cross- lingual NLP tasks. We introduce the first un- supervised approach to this problem, learn- ing clusters of semantically equivalent English and French relations between referring expres- sions, based on their named-entity arguments in large monolingual corpora. The clusters can be used as language-independent semantic relations, by mapping clustered expressions in different languages onto the same relation. Our approach needs no parallel text for train- ing, but outperforms a baseline that uses ma- chine translation on a cross-lingual question answering task. We also show how to use the semantics to improve the accuracy of machine translation, by using it in a simple reranker.

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