Learning Entity Representation for Entity Disambiguation
Zhengyan He, Shujie Liu, Mu Li, Ming Zhou, Houfeng Wang and Longkai Zhang
The 51st Annual Meeting of the Association for Computational Linguistics - Short Papers (ACL Short Papers 2013)
Sofia, Bulgaria, August 4-9, 2013
We propose a novel entity disambiguation model based on Deep Neural Network (DNN). Instead of utilizing simple similarity measure and disjoint combinations of such measures, our method directly optimizes document and entity representation 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.
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