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

机译：用于深入了解技术文件的人类SPN方法

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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.

机译：本文涉及技术文件的自动深度理解（ADU）。在这里，我们在两种不同的方式之间具有协同协作，自然语言文本理解（NLU）方法和图 - 图像提取和建模（DIM）一个，用于深入了解技术文档。特别是，NLU从文档中提取文本并通过创建随机Petri-Net（SPN）图模型来确定名词和交互之间的关联。 DIM从文档中提取图表并生成其图形模型。然后我们以协同方式结合（关联）这两个模型，这导致了对技术文件的更深入了解。

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《IEEE International Conference on Tools with Artificial Intelligence》|2016年|xxix 576 p. :|共7页
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N. Bourbakis; A. Psarologou; G. Rematska; A. Esposito;
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中图分类人工智能理论;
关键词
Kernel; Optical character recognition software; Engines; Natural languages; Text recognition; Reverse engineering; Image processing;

机译：内核;光学字符识别软件;发动机;自然语言;文本识别;逆向工程;图像处理;

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

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