Identification of Topical Entities in Discourse: a Connectionist Approach to Attentional Mechanisms in Language

Abstract

This paper describes a connectionist network which uses surface features and minimal semantic information to identify topic entities within the framework of a simplified representation of the nominal expressions in written narrative discourse. This process is seen as an attentional function critical for interpreting subsequent text and understanding the discourse as a whole. The model also addresses pronoun resolution as a function of attentional state. Since topic entity selection depends heavily on sequentially presented semantic, syntactic and contextual information from the preceding narrative, the model develops an internal representation which incorporates the relevant features of past events in addition to useful features of the current input.