  This paper presents a simple and computationally efficient approach for
entity linking (EL), compared with recurrent neural networks (RNNs) or
convolutional neural networks (CNNs), by making use of feedforward neural
networks (FFNNs) and the recent dual fixed-size ordinally forgetting encoding
(dual-FOFE) method to fully encode the sentence fragment and its left/right
contexts into a fixed-size representation. Furthermore, in this work, we
propose to incorporate PageRank based distillation in our candidate generation
module. Our neural linking models consist of three parts: a PageRank based
candidate generation module, a dual-FOFE-net neural ranking model and a simple
NIL entity clustering system. Experimental results have shown that our proposed
neural linking models achieved higher EL accuracy than state-of-the-art models
on the TAC2016 task dataset over the baseline system, without requiring any
in-house data or complicated handcrafted features. Moreover, it achieves a
competitive accuracy on the TAC2017 task dataset.
