Papers by Chun-Ta Lu
MZET: Memory Augmented Zero-Shot Fine-grained Named Entity Typing (2020.coling-main)
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| Challenge: | Named entity typing (NET) is a classification task of assigning an entity mention in the context with given semantic types. |
| Approach: | They propose a memory-augmented FNET model to tackle unseen types in a zero-shot manner. |
| Outcome: | The proposed model outperforms the state-of-the-art models with up to 8% gain in Micro-F1 and Macro-F1. |