  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.
Besides, MZET considers the semantic meaning and the hierarchical structure
into the entity type representation. Finally, through the memory component
which models the relationship between the entity mention and the entity type,
MZET transfer the knowledge from seen entity types to the zero-shot ones.
Extensive experiments on three public datasets show prominent performance
obtained by MZET, which surpasses the state-of-the-art FNET neural network
models with up to 7% gain in Micro-F1 and Macro-F1 score.
