A Human-Like SPN Methodology for Deep Understanding of Technical Documents

摘要

This paper deals with the Automatic Deep Understanding (ADU) of technical documents. Here we present a synergistic collaboration between two different modalities, a natural language text understanding (NLU) method and a diagram-image extraction & modeling (DIM) one for the deep understanding of technical documents. In particular, the NLU extracts the text from the document and determines the associations among the nouns and their interactions, by creating their stochastic Petri-net (SPN) graph model. The DIM extracts the diagrams from the document and produces their graph models. Then we combine (associate) these two models in a synergistic way, which leads to the deeper understanding of the technical document.