Hierarchical Losses and New Resources for Fine-grained Entity Typing and Linking

@inproceedings{Murty2018HierarchicalLA,
  title={Hierarchical Losses and New Resources for Fine-grained Entity Typing and Linking},
  author={Shikhar Murty and Pat Verga and Luke Vilnis and Irena Radovanovic and Andrew McCallum},
  booktitle={Annual Meeting of the Association for Computational Linguistics},
  year={2018}
}
Extraction from raw text to a knowledge base of entities and fine-grained types is often cast as prediction into a flat set of entity and type labels, neglecting the rich hierarchies over types and entities contained in curated ontologies. [] Key Result In experiments on all three datasets we show substantial gains from hierarchy-aware training.
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