2022, Volume 23, Issue 11
Frontiers of Information Technology & Electronic Engineering >> 2022, Volume 23, Issue 11 doi: 10.1631/FITEE.2100495
Improving entity linking with two adaptive features
广东工业大学计算机学院，中国广州市，510006
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Abstract
(EL) is a fundamental task in natural language processing. Based on neural networks, existing systems pay more attention to the construction of the , but ignore latent semantic information in the and the acquisition of effective information. In this paper, we propose two , in which the first adaptive feature enables the local and s to capture latent information, and the second adaptive feature describes effective information for embeddings. These can work together naturally to handle some uncertain information for EL. Experimental results demonstrate that our EL system achieves the best performance on the AIDA-B and MSNBC datasets, and the best average performance on out-domain datasets. These results indicate that the proposed , which are based on their own diverse contexts, can capture information that is conducive for EL.
Keywords
Entity linking ; Local model ; Global model ; Adaptive features ; Entity type
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