Combining Distant and Partial Supervision for Relation Extraction
Gabor Angeli, Julie Tibshirani, Jean Y. Wu, Christopher D. Manning · 2014
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 F 1 on the 2013 KBP Slot Filling evaluation, yielding a net F 1 of 37.7%.