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Fusing external knowledge resources for natural language understanding techniques: A survey

Wang, Yuqi; Wang, Wei; Chen, Qi; Huang, Kaizhu; Nguyen, Anh; De, Suparna; Hussain, Amir


Yuqi Wang

Wei Wang

Qi Chen

Kaizhu Huang

Anh Nguyen

Suparna De


Knowledge resources, e.g. knowledge graphs, which formally represent essential semantics and information for logic inference and reasoning, can compensate for the unawareness nature of many natural language processing techniques based on deep neural networks. This paper provides a focused review of the emerging but intriguing topic that fuses quality external knowledge resources in improving the performance of natural language processing tasks. Existing methods and techniques are summarised in three main categories: (1) static word embeddings, (2) sentence-level deep learning models, and (3) contextualised language representation models, depending on when, how and where external knowledge is fused into the underlying learning models. We focus on the solutions to mitigate two issues: knowledge inclusion and inconsistency between language and knowledge. Details on the design of each representative method, as well as their strength and limitation, are discussed. We also point out some potential future directions in view of the latest trends in natural language processing research.

Journal Article Type Article
Acceptance Date Nov 24, 2022
Online Publication Date Nov 28, 2022
Publication Date 2023-04
Deposit Date Mar 10, 2023
Journal Information Fusion
Print ISSN 1566-2535
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 92
Pages 190-204
Keywords Natural language understanding, Knowledge graph, Knowledge fusion, Representation learning, Deep learning