As entity type systems become richer and more fine-grained, we expect the number of types assigned to a given entity to increase. However, most fine-grained typing work has focused on datasets that exhibit a low degree of type multiplicity. In this paper, we consider the high-multiplicity regime inherent in data sources such as Wikipedia that have semi-open type systems. We introduce a set-prediction approach to this problem and show that our model outperforms unstructured baselines on a new Wikipedia-based fine-grained typing corpus.
@article{arxiv.1704.07751,
title = {Fine-Grained Entity Typing with High-Multiplicity Assignments},
author = {Maxim Rabinovich and Dan Klein},
journal= {arXiv preprint arXiv:1704.07751},
year = {2017}
}