Distant Supervision for Relation Extraction via Piecewise Convolutional Neural Networks

Two problems arise when using distant supervision for relation extraction. First, in this method, an already existing knowledge base is heuristically aligned to texts, and the alignment results are treated as labeled data. However, the heuristic alignment can fail, resulting in wrong label problem. In addition, in previous approaches, statistical models have typically been applied to ad hoc features. The noise that originates from the feature extraction process can cause poor performance.

• NN
  National Natural Science Foundation of China

  Awards: 61272332, 61202329
• NK
  National Key Research and Development Program of China

  Award: 2014CB340503