We propose a novel entity disambiguation model, based on Deep Neural Network (DNN). Instead of utilizing simple similarity measures and their disjoint combinations, our method directly optimizes document and entity representations for a given similarity measure. Stacked Denoising Auto-encoders are first employed to learn an initial document representation in an unsupervised pre-training stage. A supervised fine-tuning stage follows to optimize the representation towards the similarity measure. Experiment results show that our method achieves state-of-the-art performance on two public datasets without any manually designed features, even beating complex collective approaches.
In 2013, He et al. REF proposed an entity disambiguation model, based on Deep Neural Networks.
Learning Entity Representation for Entity Disambiguation
ACL
Computer Science