A Synthesis of Stochastic Petri Net (SPN) Graphs for Natural Language Understanding (NLU) Event/Action Association

This paper focuses on the combination of StochasticPetri Net (SPN) graphs for event association in the context ofNatural Languages Understanding (NLU). Our general goal isto develop a new NLU methodology. In this paper we presentsome of its components which are: the use of AnaphoraResolution (AR), the extraction of kernel(s) based on thestructure of their parse trees, and the synthesis of SPN graphs.Each extracted kernel is represented with SPN graphs. Herewe define when, why and how to synthesize the produced SPNgraphs for event/action association, while preserving theirtiming and information flow. Finally, we provide examples ofcombined and uncombined SPN graphs of different NL textscreated using our proposed methodology.