Inspired by recent work in learning representations for natural language tasks (Collobert et al., 2011), we explore neural network models which take only raw, unconjoined features as input, and attempt to learn intermediate representations automatically.
the model we describe attempts to create independent feature representations useful for both detecting the anaphoricity of a mention (that is, whether or not a mention is anaphoric) and ranking the potential antecedents of an anaphoric mention.