Distant supervision relation extraction based on mutual information and multi-level attention

Abstract

Distant supervision for relation extraction, an effective method to reduce labor costs, has been widely used to search for novel relational facts from text. However, distant supervision always suffers from incorrect labelling problems. Meanwhile, existing methods for noise reduction oftentimes ignore the commonalities in the instances. To alleviate this issue, we propose a distant supervision relation extraction model based on mutual information and multi-level attention. In our proposed method, we calculate mutual information based on the attention mechanism. Mutual information are used to build attention at both word and sentence levels, which is expected to dynamically reduce the influence of noisy instances. Extensive experiments using a benchmark dataset have validated the effectiveness of our proposed method.