Learning a Cross-Lingual Semantic Representation of Relations Expressed in Text

Learning cross-lingual semantic representations of relations from textual data is useful for tasks like cross-lingual information retrieval and question answering. So far, research has been mainly focused on cross-lingual entity linking, which is confined to linking between phrases in a text document and their corresponding entities in a knowledge base but cannot link to relations. In this paper, we present an approach for inducing clusters of semantically related relations expressed in text, where relation clusters i can be extracted from text of different languages, ii are embedded in a semantic representation of the context, and iii can be linked across languages to properties in a knowledge base. This is achieved by combining multi-lingual semantic role labeling SRL with cross-lingual entity linking followed by spectral clustering of the annotated SRL graphs. With our initial implementation we learned a cross-lingual lexicon of relation expressions from English and Spanish Wikipedia articles. To demonstrate its usefulness we apply it to cross-lingual question answering over linked data.