Hierarchical Entity Typing via Multi-level Learning to Rank

Abstract

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

Topics & Concepts