Paper: Combining Distant and Partial Supervision for Relation Extraction

ACL ID D14-1164
Title Combining Distant and Partial Supervision for Relation Extraction
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2014
Authors

Broad-coverage relation extraction either requires expensive supervised training data, or suffers from drawbacks inherent to distant supervision. We present an ap- proach for providing partial supervision to a distantly supervised relation extrac- tor using a small number of carefully se- lected examples. We compare against es- tablished active learning criteria and pro- pose a novel criterion to sample examples which are both uncertain and representa- tive. In this way, we combine the ben- efits of fine-grained supervision for diffi- cult examples with the coverage of a large distantly supervised corpus. Our approach gives a substantial increase of 3.9% end- to-end F 1 on the 2013 KBP Slot Filling evaluation, yielding a net F 1 of 37.7%.