Improving entity linking with two adaptive features

Entity linking(EL)is a fundamental task in natural language processing.Based on neural networks,existing systems pay more attention to the construction of the global model,but ignore latent semantic information in the local model and the acquisition of effective entity type information.In this paper,we propose two adaptive features,in which the first adaptive feature enables the local and global models to capture latent information,and the second adaptive feature describes effective information for entity type embeddings.These adaptive features can work together naturally to handle some uncertain entity type 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 adaptive features,which are based on their own diverse contexts,can capture information that is conducive for EL.