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- W4310231311 abstract "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." @default.
- W4310231311 created "2022-11-30" @default.
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- W4310231311 creator A5084232117 @default.
- W4310231311 date "2023-04-01" @default.
- W4310231311 modified "2023-09-27" @default.
- W4310231311 title "Fusing external knowledge resources for natural language understanding techniques: A survey" @default.
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- W4310231311 doi "https://doi.org/10.1016/j.inffus.2022.11.025" @default.
- W4310231311 hasPublicationYear "2023" @default.
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