Improving distant supervision using inference learning
This addresses the issue of lower performance in relation extraction systems due to noisy training data, but it is incremental as it builds on existing distant supervision methods.
The paper tackles the problem of errors in distantly supervised data for relation extraction by proposing a method to detect false negatives using knowledge inference, resulting in improved system performance.
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 negative training examples using a knowledge inference method. Results show that our approach improves the performance of relation extraction systems trained using distantly supervised data.