Fine-Grained Entity Typing via Hierarchical Multi Graph Convolutional Networks

Hailong Jin, Lei Hou, Juanzi Li, Tiansi Dong

Abstract
This paper addresses the problem of inferring the fine-grained type of an entity from a knowledge base. We convert this problem into the task of graph-based semi-supervised classification, and propose Hierarchical Multi Graph Convolutional Network (HMGCN), a novel Deep Learning architecture to tackle this problem. We construct three kinds of connectivity matrices to capture different kinds of semantic correlations between entities. A recursive regularization is proposed to model the subClassOf relations between types in given type hierarchy. Extensive experiments with two large-scale public datasets show that our proposed method significantly outperforms four state-of-the-art methods.
Anthology ID:
D19-1502
Volume:
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
Month:
November
Year:
2019
Address:
Hong Kong, China
Editors:
Kentaro Inui, Jing Jiang, Vincent Ng, Xiaojun Wan
Venues:
EMNLP | IJCNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
4969–4978
Language:
URL:
https://aclanthology.org/D19-1502
DOI:
10.18653/v1/D19-1502
Bibkey:
Cite (ACL):
Hailong Jin, Lei Hou, Juanzi Li, and Tiansi Dong. 2019. Fine-Grained Entity Typing via Hierarchical Multi Graph Convolutional Networks. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 4969–4978, Hong Kong, China. Association for Computational Linguistics.
Cite (Informal):
Fine-Grained Entity Typing via Hierarchical Multi Graph Convolutional Networks (Jin et al., EMNLP-IJCNLP 2019)
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https://preview.aclanthology.org/nschneid-patch-4/D19-1502.pdf
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