Modeling Natural Language Sentences into SPN Graphs

Natural language processing and understanding is an attractive field and many techniques and tools for document processing have been developed. Most of the techniques use either statistical models or graph-based approaches. Here we present the modeling of a methodology based on stochastic Petri-nets (SPN) to explain the transformation of a natural language (NL) sentence into a state machine representation as stated in [16]. In particular, we initially convert NL sentences into graphs using the (Agent → Action → Patient) kernel and then we convert the graphs into SPN graph descriptions in order to efficiently offer a model of semantically represent and understand natural language events of a document. The selection of the SPN graph model is due to its capability for efficiently representing structural and functional knowledge.