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

2020-06-02

Proceedings of the AAAI Conference on Artificial Intelligence, 34

Volume

Vol. 34 No. 09: Issue 9: EAAI-20 / AAAI Special Programs

Demonstration Track

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.

10.1609/aaai.v34i09.7104