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Measuring text complexity is an essential task in several fields andapplications (such as NLP, semantic web, smart education, etc.). The semanticlayer of text is more tacit than its syntactic structure and, as a result,calculation of semantic complexity is more difficult than syntactic complexity.While there are famous and powerful academic and commercial syntacticcomplexity measures, the problem of measuring semantic complexity is still achallenging one. In this paper, we introduce the DAST model, which stands forDeciding About Semantic Complexity of a Text. DAST proposes an intuitionisticapproach to semantics that lets us have a well-defined model for the semanticsof a text and its complexity: semantic is considered as a lattice of intuitionsand, as a result, semantic complexity is defined as the result of a calculationon this lattice. A set theoretic formal definition of semantic complexity, as a6-tuple formal system, is provided. By using this formal system, a method formeasuring semantic complexity is presented. The evaluation of the proposedapproach is done by a set of three human-judgment experiments. The results showthat DAST model is capable of deciding about semantic complexity of text.Furthermore, the analysis of the results leads us to introduce a Markovianmodel for the process of common-sense, multiple-steps and semantic-complexityreasoning in people. The results of Experiments demonstrate that our methodoutperforms the random baseline with improvement in better precision andcompetes with other methods by less error percentage.

DAST Model: Deciding About Semantic Complexity of a Text