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- sl:arxiv_author :
- sl:arxiv_firstAuthor : Yasumasa Onoe
- sl:arxiv_num : 2101.00345
- sl:arxiv_published : 2021-01-02T00:59:10Z
- sl:arxiv_summary : Neural entity typing models typically represent fine-grained entity types as
vectors in a high-dimensional space, but such spaces are not well-suited to
modeling these types' complex interdependencies. We study the ability of box
embeddings, which embed concepts as d-dimensional hyperrectangles, to capture
hierarchies of types even when these relationships are not defined explicitly
in the ontology. Our model represents both types and entity mentions as boxes.
Each mention and its context are fed into a BERT-based model to embed that
mention in our box space; essentially, this model leverages typological clues
present in the surface text to hypothesize a type representation for the
mention. Box containment can then be used to derive both the posterior
probability of a mention exhibiting a given type and the conditional
probability relations between types themselves. We compare our approach with a
vector-based typing model and observe state-of-the-art performance on several
entity typing benchmarks. In addition to competitive typing performance, our
box-based model shows better performance in prediction consistency (predicting
a supertype and a subtype together) and confidence (i.e., calibration),
demonstrating that the box-based model captures the latent type hierarchies
better than the vector-based model does.@en
- sl:arxiv_title : Modeling Fine-Grained Entity Types with Box Embeddings@en
- sl:arxiv_updated : 2021-06-03T05:51:55Z
- sl:bookmarkOf : https://arxiv.org/abs/2101.00345
- sl:creationDate : 2021-06-22
- sl:creationTime : 2021-06-22T13:40:30Z
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