PARTNER: Human-in-the-Loop Entity Name Understanding with Deep Learning


  • Kun Qian IBM Research - Almaden
  • Poornima Chozhiyath Raman IBM Research - Almaden
  • Yunyao Li IBM Research - Almaden
  • Lucian Popa IBM Research - Almaden



Entity name disambiguation is an important task for many text-based AI tasks. Entity names usually have internal semantic structures that are useful for resolving different variations of the same entity. We present, PARTNER, a deep learning-based interactive system for entity name understanding. Powered by effective active learning and weak supervision, PARTNER can learn deep learning-based models for identifying entity name structure with low human effort. PARTNER also allows the user to design complex normalization and variant generation functions without coding skills.




How to Cite

Qian, K., Chozhiyath Raman, P., Li, Y., & Popa, L. (2020). PARTNER: Human-in-the-Loop Entity Name Understanding with Deep Learning. Proceedings of the AAAI Conference on Artificial Intelligence, 34(09), 13634-13635.