Fine-Grained Entity Typing via Hierarchical Multi Graph Convolutional Networks

Author: J. HaiLong, H. Lei, L. Juanzi, T. Dong
Journal: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
Year: 2019

Citation information

J. HaiLong, H. Lei, L. Juanzi, T. Dong,
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP),
2019,
4969-4978,
November,
Association for Computational Linguistics,
Hong Kong, China,
http://dx.doi.org/10.18653/v1/D19-1502

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.