Entity disambiguation with memory network

Abstract

Abstract We develop a computational approach based on memory network for entity disambiguation. The approach automatically finds important clues of a mention from surrounding contexts with attention mechanism, and leverages these clues to facilitate entity disambiguation. Unlike existing feature-based methods, this approach does not rely on any manually designed features. Unlike existing neural models such as recurrent or convolutional neural network, this approach leverages the importance of context words in an explicit way. The model could be easily trained with back-propagation. To effectively learn the model parameters, we automatically collect large-scale mention-entity pairs from Wikipedia as training data. We verify the effectiveness of the proposed approach on a benchmark dataset from TAC-KBP evaluation in 2010. Experimental results demonstrate that our approach empirically surpasses strong feature based and neural network based methods. Model analysis further reveals that our approach has the capacity to discover important clues from contexts.