HyperExpan: Taxonomy Expansion with Hyperbolic Representation Learning
- URL: http://arxiv.org/abs/2109.10500v1
- Date: Wed, 22 Sep 2021 03:27:04 GMT
- Title: HyperExpan: Taxonomy Expansion with Hyperbolic Representation Learning
- Authors: Mingyu Derek Ma, Muhao Chen, Te-Lin Wu and Nanyun Peng
- Abstract summary: We present HyperExpan, a taxonomy expansion algorithm that learns to represent concepts and their relations with a Hyperbolic Graph Neural Network (HGNN)
Experiments show that our proposed HyperExpan outperforms baseline models with representation learning in a Euclidean feature space and achieves state-of-the-art performance on the taxonomy expansion benchmarks.
- Score: 24.080321524759455
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Taxonomies are valuable resources for many applications, but the limited
coverage due to the expensive manual curation process hinders their general
applicability. Prior works attempt to automatically expand existing taxonomies
to improve their coverage by learning concept embeddings in Euclidean space,
while taxonomies, inherently hierarchical, more naturally align with the
geometric properties of a hyperbolic space. In this paper, we present
HyperExpan, a taxonomy expansion algorithm that seeks to preserve the structure
of a taxonomy in a more expressive hyperbolic embedding space and learn to
represent concepts and their relations with a Hyperbolic Graph Neural Network
(HGNN). Specifically, HyperExpan leverages position embeddings to exploit the
structure of the existing taxonomies, and characterizes the concept profile
information to support the inference on unseen concepts during training.
Experiments show that our proposed HyperExpan outperforms baseline models with
representation learning in a Euclidean feature space and achieves
state-of-the-art performance on the taxonomy expansion benchmarks.
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