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Named entity typing (NET) is a classification task of assigning an entitymention in the context with given semantic types. However, with the growingsize and granularity of the entity types, rare researches in previous concernwith newly emerged entity types. In this paper, we propose MZET, a novel memoryaugmented FNET (Fine-grained NET) model, to tackle the unseen types in azero-shot manner. MZET incorporates character-level, word-level, andcontextural-level information to learn the entity mention representation.Besides, MZET considers the semantic meaning and the hierarchical structureinto the entity type representation. Finally, through the memory componentwhich 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 performanceobtained by MZET, which surpasses the state-of-the-art FNET neural networkmodels with up to 7% gain in Micro-F1 and Macro-F1 score.

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