Abstract
We propose crosslingual distant supervision (crosslingual DS) for relation extraction, an approach that automatically extracts labels from a pivot language for labeling one or more target languages. The approach has two benefits compared to standard DS: (i) increased coverage if target language labels are not available; and (ii) higher accuracy of automatically generated labels because noisy labels are eliminated in crosslingual filtering. An evaluation for two relations of different complexity shows that crosslingual DS increases the accuracy of relation extraction. Our approach is language independent; we successfully apply it to four different languages: Chinese, English, French and German.
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