Position-aware Attention and Supervised Data Improve Slot Filling

Organized relational knowledge in the form of "knowledge graphs" is important for many applications. However, the ability to populate knowledge bases with facts automatically extracted from documents has improved frustratingly slowly. This paper simultaneously addresses two issues that have held back prior work. We first propose an effective new model, which combines an LSTM sequence model with a form of entity position-aware attention that is better suited to relation extraction. Then we build TACRED, a large (119,474 examples) supervised relation extraction dataset, obtained via crowdsourcing and targeted towards TAC KBP relations. The combination of better supervised data and a more appropriate…

• AI
  Allen Institute for Artificial Intelligence
• DA
  Defense Advanced Research Projects Agency

  Award: FA8750
• AR
  Advanced Research Projects Agency
• AF
  Air Force Research Laboratory