腾讯 AI Lab 官网

腾讯 AI Lab 官网
Bootstrapping Distantly Supervised IE using Joint Learning and Small Well-structured Corpora
Abstract View the Paper
We propose a framework to improve the performance of distantly-supervised relation extraction, by jointly learning to solve two related tasks: concept-instance extraction and relation extraction. We further extend this framework to make a novel use of document structure: in some small, well structured corpora, sections can be identified that correspond to relation arguments, and distantly-labeled examples from such sections tend to have good precision. Using these as seeds we extract additional relation examples by applying label propagation on a graph composed of noisy examples extracted from a large unstructured testing corpus. Combined with the soft constraint that concept examples should have the same type as the second argument of the relation, we get significant improvements over several state-of-the-art approaches to distantly-supervised relation extraction, and reasonable extraction performance even with very small set of distant labels.
Venue
In Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence (AAAI 2017), San Francisco, California, USA
Publication Time
Feb 2017
Authors
Lidong Bing, Bhuwan Dhingra, Kathryn Mazaitis, Jong Hyuk Park, and William W. Cohen