Converting Natural Language Text Sentences Into SPN Representations for Associating Events

Abstract

A better understanding of events many times requires the association and the e±cient representation of multi-modal information. A good approach to this important issue is the development of a common platform for converting di®erent modalities (such as images, text, etc.) into the same medium and associating them for e±cient processing and understanding. In a previous paper we have presented a Local-Global graph model for the conversion of images into graphs with attributes and then into natural language (NL) text sentences [25]. Here, in this paper we propose the conversion of NL text sentences into graphs and then into Stochastic Petri-nets (SPN) descriptions in order to e±ciently o®er a model of associating \activities or changes" in multimodal information for events representation and understanding. The selection of the SPN graph model is due to its capability for e±ciently representing structural and functional knowledge. Simple illustrative examples are provided for proving the concept proposed here.