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Natural Language Understanding (NLU) is an old and really challenging field with a variety of research work published on it. In this paper we present a formal language methodology based on a state machine for efficiently representing natural language events/actions and their associations in well-written documents. The methodology consists of the following steps. We firstly apply Anaphora Resolution (AR) to the pre-processing natural language text. Then we extract the kernel(s) of each sentence. These kernels are formally represented using a formal language, (Glossa) to map the language expressions (kernels) into Stochastic Petri Nets (SPN) graphs. Finally we apply a set of rules to combine the SPN graphs in order to achieve the associations of actions/events in time. Special emphasis of this paper is the mapping of kernels of NL sentences into SPN graphs. Note that this work does not cover all the aspects of the NLU. Examples of SPN graphs of different NL texts, produced by our proposed methodology are given.

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