In this paper, we present a computational model for transforming discourses into Quasi-Mental Clusters (QMCs) through a convergence process. The process is interpreted as a particular transformation of a given set of discourse segments and concepts by examining the textual continuity. Examinations include testing the local cohesion in a cohesion parsing as well as the golbal coherence in semantic decomposition. In the convergence process, sentences in a discourse are represented as nodes in aspreading activation network. Competing coalitions of the nodes drive the network into a stable equilitrium. We argue the resulting QMCs are useful data structures in remembrance, summarization and knowledge discovery in discourses.
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