… , and neural models have achieved recent success in this task. However, most existing methods
rely on local contexts to resolve entities … a novel neural model for collective entity linking, …

… collective EL … for collective entity disambiguation; (2) We propose a novel end-to-end
graph-based NN model which incorporates the external KB to collectively infer the referent entities …

… Collective Entity linking maps mentions of entities in document to corresponding entries in …
entity relations. This course project aims at handling collective entity linking via Graph Neural …

… ) based on Probability Model [32], Deep neuron network for entity linking task (DNNEL)
based on Neural Networks [17], and Entity linking via listwise ranking and SVM classification (…

… , this paper proposes a graph-based collective entity linking method, which can effectively
and … a purely collective inference algorithm, which can jointly infer the referent entities of all …

… Joint learning of local and global features for entity linking via neural networks. In:
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: …

… of neural entity linking approaches from the most general architecture of EL pipelines and
continue with various specific modifications like joint entity mention detection and linking, …

… similarity for entity linking with convolutional neural networks. … context and entity with neural
networks for entity disambiguation… Neural collective entity linking based on recurrent random …

… linking accuracy, list-only entity linking can merely take advantage of co-occurrences information
in entity lists. … and deep neural networks to better model mention-entity compatibility and …

… Collective entity disambiguation, or collective entity linking aims to jointly resolve multiple
mentions by linking them to their associated entities … for entity linking with convolutional neural …