Hierarchical Entity Typing via Multi-level Learning to Rank

• URL: http://arxiv.org/abs/2004.02286v1
• Date: Sun, 5 Apr 2020 19:27:18 GMT
• Title: Hierarchical Entity Typing via Multi-level Learning to Rank
• Authors: Tongfei Chen, Yunmo Chen, Benjamin Van Durme
• Abstract summary: 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 coarse-to-fine decoder that restricts viable candidates at each level of the ontology based on already predicted parent type(s)
• Score: 38.509244927293715
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 coarse-to-fine decoder that restricts viable candidates at each level of the ontology based on already predicted parent type(s). We achieve state-of-the-art across multiple datasets, particularly with respect to strict accuracy.

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