PARTNER: Human-in-the-Loop Entity Name Understanding with Deep Learning - Qian, Kun; Chozhiyath Raman, Poornima; Li, Yunyao; Popa, Lucian (Proceedings of the AAAI Conference on Artificial Intelligence, 2020)
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.