Hierarchical Entity Typing via Multi-level Learning to Rank
Tongfei Chen, Yunmo Chen, Benjamin Van Durme
Information Extraction Long Paper
Session 14B: Jul 8
(18:00-19:00 GMT)
Session 15B: Jul 8
(21:00-22:00 GMT)
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). Our approach significantly outperform prior work on strict accuracy, demonstrating the effectiveness of our method.
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