Filling knowledge base gaps for distant supervision of relation extraction

Distant supervision has attracted recent interest for training information extraction systems because it does not require any human annotation but rather isting knowledge bases to he bel a training corpus. Howe work has failed to address of false negative training exa beled due to the incompleten edge bases. To tackle this propose a simple yet novel fr combines a passage retrieval coarse features into a state-o tion extractor using multi-in ing with fine features. We formation retrieval techniqu relevance feedback to expan bases, assuming entity pairs i passages are more likely to e tion. Our proposed technique improves the quality of dis vised relation extraction, bo from 47.7% to 61.2% with a high level of precision of around 93% in the experiments.