Neural Relation Extraction with Selective Attention over Instances

Distant supervised relation extraction has been widely used to find novel relational facts from text. However, distant supervision inevitably accompanies with the wrong labelling problem, and these noisy data will substantially hurt the performance of relation extraction. To alleviate this issue, we propose a sentence-level attention-based model for relation extraction. In this model, we employ convolutional neural networks to embed the semantics of sentences. Afterwards, we build sentence-level attention over multiple instances, which is expected to dynamically reduce the weights of those noisy instances. Experimental results on real-world datasets show that, our model can make full use of all informative…

• NN
  National Natural Science Foundation of China

  Awards: 61572273, 61303075
• TU
  Tsinghua University
• NK
  National Key Research and Development Program of China

  Award: 2014CB340501