Hierarchical Entity Typing via Multi-level Learning to Rank
Tongfei Chen, Yunmo Chen, Benjamin Van Durme · 2020
We propose a novel method for hierarchical entity classification that embraces ontological structure at both training and during prediction.At training, our novel multi-level learning-to-rank loss compares positive types against negative siblings according to the type tree.During prediction, we define a coarseto-fine decoder that restricts viable candidates at each level of the ontology based on already predicted parent type(s).We achieve stateof-the-art across multiple datasets, particularly with respect to strict accuracy.1