MZET: Memory Augmented Zero-Shot Fine-grained Named Entity Typing

Understand

Named entity typing (NET) is a classification task of assigning an entity mention in the context with given semantic types.

• However, with the growing size and granularity of the entity types, rare researches in previous concern with newly emerged entity types.
• In this paper, we propose MZET, a novel memory augmented FNET (Fine-grained NET) model, to tackle the unseen types in a zero-shot manner.
• MZET incorporates character-level, word-level, and contextural-level information to learn the entity mention representation.