Improving distant supervision using inference learning

Distant supervision is a widely applied approach to automatic training of relation extraction systems and has the advantage that it can generate large amounts of labelled data with minimal effort. However, this data may contain errors and consequently systems trained using distant supervision tend not to perform as well as those based on manually labelled data. This work proposes a novel method for detecting potential false negatives in automatically generated training data using a knowledge inference method. Results show that our approach improves the performance of relation extraction systems trained using distantly supervised data.

• Type: Journal
• Year: 2015
• Where: ACL
• Authors: Roland Roller, Eneko Agirre, Aitor Soroa, Mark Stevenson
• Keywords: ACL 2015, Computational Linguistics