Learning the Information Status of Noun Phrases in Spoken Dialogues
Altaf Rahman, Vincent Ng · 2011
An entity in a dialogue may be old, new, or mediated/inferrable with respect to the hearer’s beliefs. Knowing the information status of the entities participating in a dialogue can therefore facilitate its interpretation. We address the under-investigated problem of automatically determining the information status of discourse entities. Specifically, we extend Nissim’s (2006) machine learning approach to information-status determination with lexical and structured features, and exploit learned knowledge of the information status of each discourse entity for coreference resolution. Experimental results on a set of Switchboard dialogues reveal that (1) incorporating our proposed features into Nissim’s feature set enables our system to achieve stateof-the-art performance on information-status classification, and (2) the resulting information can be used to improve the performance of learning-based coreference resolvers. 1