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.

本文提出了一种新的记忆增强 FNET 模型，名为 MZET，以零样本学习的方式解决新出现的实体类型问题，在字符级、词级和上下文级信息中学习实体表述，并将语义含义和层次结构纳入实体类型表述中，通过内存组件将知识从已见实体类型传递到零样本中，并在三个公共数据集上表现出优越的性能，技术水平超过 FNET 神经网络模型，Micro-F1 和 Macro-F1 得分最高可达 7%。