Combining Distant and Partial Supervision for Relation Extraction

Gabor Angeli, Julie Tibshirani, Jean Wu, Christopher D. Manning

Abstract: Broad-coverage relation extraction either requires expensive supervised training data, or suffers from drawbacks inherent to distant supervision. We present an approach for providing partial supervision to a distantly supervised relation extractor using a small number of carefully selected examples. We compare against established active learning criteria and propose a novel criterion to sample examples which are both uncertain and representative. In this way, we combine the benefits of fine-grained supervision for difficult examples with the coverage of a large distantly supervised corpus. Our approach gives a substantial increase of 3.9% endto-end F1 on the 2013 KBP Slot Filling evaluation, yielding a net F1 of 37.7%.