Our approach consists of the following steps: (1) generating high quality embedding for each training instance; (2) pre-training with the stacked denoising auto-encoder (Bengio et al., 2003) for feature dimension reduction; and (3) supervised fine-tuning to optimize the neural networks towards a similarity measure (e.g., dot product).
morph resolution is significantly different from the traditional entity linking task since the latter mainly focuses on formal and explicit entities (e.g., "薄 熙 来 (Bo Xilai)") which tend to have stable referents in Wikipedia.