Investigating the Role of Prior Disambiguation in Deep-learning   Compositional Models of Meaning
  This paper aims to explore the effect of prior disambiguation on neural network- based compositional models, with the hope that better semantic representations for text compounds can be produced. We disambiguate the input word vectors before they are fed into a compositional deep net. A series of evaluations shows the positive effect of prior disambiguation for such deep models. 
Jianpeng Cheng, Dimitri Kartsaklis, Edward Grefenstette
cs.CL cs.LG cs.NE