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

• URL: http://arxiv.org/abs/2004.01267v2
• Date: Mon, 2 Nov 2020 19:11:29 GMT
• Title: MZET: Memory Augmented Zero-Shot Fine-grained Named Entity Typing
• Authors: Tao Zhang, Congying Xia, Chun-Ta Lu, Philip Yu
• Abstract summary: Named entity typing (NET) is a classification task of assigning an entity mention in the context with given semantic types. We propose MZET, a novel memory augmented FNET 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.
• Score: 11.88688584631821
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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