A Hierarchical Hyper-rectangle Mass Model for Fine-grained Entity Typing

SHING HU, Chunyu Li, Bibo Hao

Keywords: entity typing, hierarchical classification, hRMM, geometric embedding

Abstract: Fine-grained entity typing is the task of detecting types of entities inside a given language text. Entity typing models typically transform entities into vectors in high-dimensional space, hyperbolic space, or add additional context information. However, such spaces or feature transformations are not compatible with modeling types' inter-dependencies and diverse scenarios. We study the ability of the hierarchical hyper-rectangle mass model(hRMM), which represents mentions and types into hyper-rectangle mass(hRM) and thus captures the relationships of ontology into a geometric mass view. Natural language contexts are fed into the encoder and then projected to hyper-rectangle mass embedding(hRME). We find that hRM perfectly depicts features of mentions and types. With further research in hypervolume indicators and adaptive thresholds, performance achieves additional improvement. Experiments show that our approach achieves better performance on several entity typing benchmarks and attains state-of-the-art results on two benchmark datasets.

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